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S0895611114001505 | We present a fully automatic method to segment the skull from 2-D ultrasound images of the fetal head and to compute the standard biometric measurements derived from the segmented images. The method is based on the minimization of a novel cost function. The cost function is formulated assuming that the fetal skull has an approximately elliptical shape in the image and that pixel values within the skull are on average higher than in surrounding tissues. The main idea is to construct a template image of the fetal skull parametrized by the ellipse parameters and the calvarial thickness. The cost function evaluates the match between the template image and the observed ultrasound image. The optimum solution that minimizes the cost is found by using a global multiscale, multistart Nelder–Mead algorithm. The method was qualitatively and quantitatively evaluated using 90 ultrasound images from a recent segmentation grand challenge. These images have been manually analyzed by three independent experts. The segmentation accuracy of the automatic method was similar to the inter-expert segmentation variability. The automatically derived biometric measurements were as accurate as the manual measurements. Moreover, the segmentation accuracy of the presented method was superior to the accuracy of the other automatic methods that have previously been evaluated using the same data. | Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation |
S0895611114001517 | A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake. | Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm |
S0895611114001529 | Common CT imaging signs of lung diseases (CISL) play important roles in the diagnosis of lung diseases. This paper proposes a new method of multiple classifier fusion to recognize the CISLs, which is based on the confusion matrices of the classifiers and the classification confidence values outputted by the classifiers. The confusion matrix reflects the historical reliability of decision-making of a classifier, while the difference between the classification confidence values reflects the on-line reliability of its decision-making. The two factors are merged to determine the weights of the classifiers’ classification confidence values. Then the classifiers are fused in a weighted-sum form to make the final decision. We apply the proposed classifier fusion method to combine five types of classifiers for CISL recognition, including support vector machine (SVM), back-propagation neural network (BPNN), Naïve Bayes (NB), k-nearest neighbor (k-NN) and decision tree (DT). In the experiments on lung CT images, our method not only brought stable improvements of recognition performance, compared with single classifiers, but also outperformed two well-known methods of classifier fusion, AdaBoost and Bagging. These results show that the proposed method is effective and promising. | A new classifier fusion method based on historical and on-line classification reliability for recognizing common CT imaging signs of lung diseases |
S0895611114001530 | This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution. Second, multiple superpixel resolutions are integrated and unified for better cup boundary adherence. Compared to the state-of-the-art intra-image learning approach, we demonstrate improvements in optic cup localization accuracy with full cup-to-disc ratio range, while incurring only minor increase in computing cost. | Robust multi-scale superpixel classification for optic cup localization |
S0895611114001542 | We address the problem of subclassification of rare circulating cells using data driven feature selection from images of candidate circulating tumor cells from patients diagnosed with breast, prostate, or lung cancer. We determine a set of low level features which can differentiate among candidate cell types. We have implemented an image representation based on concentric Fourier rings (FRDs) which allow us to exploit size variations and morphological differences among cells while being rotationally invariant. We discuss potential clinical use in the context of treatment monitoring for cancer patients with metastatic disease. | Fourier-ring descriptor to characterize rare circulating cells from images generated using immunofluorescence microscopy |
S0895611114001554 | Interactive 3D cutting is widely used as a flexible manual segmentation tool to extract medical data on regions of interest. A novel method for clipping 3D medical data is proposed to reveal the interior of volumetric data. The 3D cutting method retains or clips away selected voxels projected inside an arbitrary-shaped closed curve which is clipping geometry constructed by interactive tool to make cutting operation more flexible. Transformation between the world and screen coordinate frames is studied to project voxels of medical data onto the screen frame and avoid computing intersection of clipping geometry and volumetric data in 3D space. For facilitating the decision on whether the voxels should be retained, voxels through coordinate transformation are all projected onto a binary mask image on screen frame which the closed curve is also projected onto to conveniently obtain the voxels of intersection. The paper pays special attention to optimization algorithm of cutting process. The optimization algorithm that mixes octree with quad-tree decomposition is introduced to reduce computation complexity, save computation time, and match real time. The paper presents results obtained from raw and segmented medical volume datasets and the process time of cutting operation. | Interactive 3D medical data cutting using closed curve with arbitrary shape |
S0895611114001566 | Ultrasound Computer Tomography (USCT) is a promising breast imaging modality under development. Comparison to a standard method like mammography is essential for further development. Due to significant differences in image dimensionality and compression state of the breast, correlating USCT images and X-ray mammograms is challenging. In this paper we present a 2D/3D registration method to improve the spatial correspondence and allow direct comparison of the images. It is based on biomechanical modeling of the breast and simulation of the mammographic compression. We investigate the effect of including patient-specific material parameters estimated automatically from USCT images. The method was systematically evaluated using numerical phantoms and in-vivo data. The average registration accuracy using the automated registration was 11.9mm. Based on the registered images a method for analysis of the diagnostic value of the USCT images was developed and initially applied to analyze sound speed and attenuation images based on X-ray mammograms as ground truth. Combining sound speed and attenuation allows differentiating lesions from surrounding tissue. Overlaying this information on mammograms, combines quantitative and morphological information for multimodal diagnosis. | Image fusion of Ultrasound Computer Tomography volumes with X-ray mammograms using a biomechanical model based 2D/3D registration |
S0895611114001578 | Interventional radiology (IR) is widely used in the treatment of cardiovascular disease. The manipulation of the guidewire and catheter is an essential skill in IR procedure. Computer-based training simulators can provide solutions to overcome many drawbacks of the traditional apprenticeship training during the procedure. In this paper, a physically-based approach to simulating the behavior of the guidewire is presented. Our approach models the guidewire as thin flexible elastic rods with different resolutions which are dynamically adaptive to the curvature of the vessel. More material characteristics of this deformable material are integrated into our discrete model to realistically simulate the behavior of the wire. A force correction strategy is proposed to adjust the elastic force to avoid endless collision detections. Several experimental tests on our simulator are given to demonstrate the effectiveness of our approach. | A robust and fast approach to simulating the behavior of guidewire in vascular interventional radiology |
S089561111400158X | Conventional in-room cone-beam computed tomography (CBCT) lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and/or adaptive treatment planning. In-room four-dimensional (4D) CBCT image acquisition is still time consuming and suffers the same issue of poor image quality. To overcome this limitation, we developed a computational framework to digitally synthesize high-quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset. A patient-specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases. Subsequently, the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration. The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient's anatomy at the time of treatment. We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in-room CBCT setup and weekly 4D CT for treatment evaluation. Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans. Various imaging artifacts were also suppressed and soft-tissue visibility was enhanced. | Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion |
S0895611114001591 | The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms. | Review of automatic pulmonary lobe segmentation methods from CT |
S0895611114001608 | This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image). | 3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach |
S0895611114001761 | Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior. | Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays |
S0895611114001785 | Autostereoscopic 3D image overlay for augmented reality (AR) based surgical navigation has been studied and reported many times. For the purpose of surgical overlay, the 3D image is expected to have the same geometric shape as the original organ, and can be transformed to a specified location for image overlay. However, how to generate a 3D image with high geometric fidelity and quantitative evaluation of 3D image's geometric accuracy have not been addressed. This paper proposes a graphics processing unit (GPU) based computer-generated integral imaging pipeline for real-time autostereoscopic 3D display, and an automatic closed-loop 3D image calibration paradigm for displaying undistorted 3D images. Based on the proposed methods, a novel AR device for 3D image surgical overlay is presented, which mainly consists of a 3D display, an AR window, a stereo camera for 3D measurement, and a workstation for information processing. The evaluation on the 3D image rendering performance with 2560×1600 elemental image resolution shows the rendering speeds of 50–60 frames per second (fps) for surface models, and 5–8 fps for large medical volumes. The evaluation of the undistorted 3D image after the calibration yields sub-millimeter geometric accuracy. A phantom experiment simulating oral and maxillofacial surgery was also performed to evaluate the proposed AR overlay device in terms of the image registration accuracy, 3D image overlay accuracy, and the visual effects of the overlay. The experimental results show satisfactory image registration and image overlay accuracy, and confirm the system usability. | Real-time computer-generated integral imaging and 3D image calibration for augmented reality surgical navigation |
S0895611114001797 | To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction. | Acceleration of MAP-EM algorithm via over-relaxation |
S0895611114001864 | The classical accelerated Demons algorithm uses Gaussian smoothing to penalize oscillatory motion in the displacement fields during registration. This well known method uses the L2 norm for regularization. Whereas the L2 norm is known for producing well behaving smooth deformation fields it cannot properly deal with discontinuities often seen in the deformation field as the regularizer cannot differentiate between discontinuities and smooth part of motion field. In this paper we propose replacement the Gaussian filter of the accelerated Demons with a bilateral filter. In contrast the bilateral filter not only uses information from displacement field but also from the image intensities. In this way we can smooth the motion field depending on image content as opposed to the classical Gaussian filtering. By proper adjustment of two tunable parameters one can obtain more realistic deformations in a case of discontinuity. The proposed approach was tested on 2D and 3D datasets and showed significant improvements in the Target Registration Error (TRE) for the well known POPI dataset. Despite the increased computational complexity, the improved registration result is justified in particular abdominal data sets where discontinuities often appear due to sliding organ motion. | Bilateral filter regularized accelerated Demons for improved discontinuity preserving registration |
S0895611114001888 | Respiratory gating has been widely applied for respiratory correction or compensation in image acquisition and image-guided interventions. A novel image-based method is proposed to extract respiratory signal directly from 2D ultrasound liver images. The proposed method utilizes a typical manifold learning method, based on local tangent space alignment based technique, to detect principal respiratory motion from a sequence of ultrasound images. This technique assumes all the images lying on a low-dimensional manifold embedding into the high-dimensional image space, constructs an approximate tangent space of each point to represent its local geometry on the manifold, and then aligns the local tangent spaces to form the global coordinate system, where the respiratory signal is extracted. The experimental results show that the proposed method can detect relatively accurate respiratory signal with high correlation coefficient (0.9775) with respect to the ground-truth signal by tracking external markers, and achieve satisfactory computing performance (2.3s for an image sequence of 256 frames). The proposed method is also used to create breathing-corrected 3D ultrasound images to demonstrate its potential application values. | A manifold learning method to detect respiratory signal from liver ultrasound images |
S089561111400189X | Volume visualization is a very important work in medical imaging and surgery plan. However, determining an ideal transfer function is still a challenging task because of the lack of measurable metrics for quality of volume visualization. In the paper, we presented the voxel vibility model as a quality metric to design the desired visibility for voxels instead of designing transfer functions directly. Transfer functions are obtained by minimizing the distance between the desired visibility distribution and the actual visibility distribution. The voxel model is a mapping function from the feature attributes of voxels to the visibility of voxels. To consider between-class information and with-class information simultaneously, the voxel visibility model is described as a Gaussian mixture model. To highlight the important features, the matched result can be obtained by changing the parameters in the voxel visibility model through a simple and effective interface. Simultaneously, we also proposed an algorithm for transfer functions optimization. The effectiveness of this method is demonstrated through experimental results on several volumetric data sets. | The voxel visibility model: An efficient framework for transfer function design |
S0895611114001906 | A guidance system using transesophageal echocardiography and magnetic tracking is presented which avoids the use of nephrotoxic contrast agents and ionizing radiation required for traditional fluoroscopically guided procedures. The aortic valve is identified in tracked biplane transesophageal echocardiography and used to guide stent deployment in a mixed reality environment. Additionally, a transapical delivery tool with intracardiac echocardiography capable of monitoring stent deployment was created. This system resulted in a deployment depth error of 3.4mm in a phantom. This was further improved to 2.3mm with the custom-made delivery tool. In comparison, the variability in deployment depth for traditional fluoroscopic guidance was estimated at 3.4mm. | Phantom study of an ultrasound guidance system for transcatheter aortic valve implantation |
S089561111400192X | A computerized framework for monitoring four-dimensional (4D) dose distributions during stereotactic body radiation therapy based on a portal dose image (PDI)-based 2D/3D registration approach has been proposed in this study. Using the PDI-based registration approach, simulated 4D “treatment” CT images were derived from the deformation of 3D planning CT images so that a 2D planning PDI could be similar to a 2D dynamic clinical PDI at a breathing phase. The planning PDI was calculated by applying a dose calculation algorithm (a pencil beam convolution algorithm) to the geometry of the planning CT image and a virtual water equivalent phantom. The dynamic clinical PDIs were estimated from electronic portal imaging device (EPID) dynamic images including breathing phase data obtained during a treatment. The parameters of the affine transformation matrix were optimized based on an objective function and a gamma pass rate using a Levenberg–Marquardt (LM) algorithm. The proposed framework was applied to the EPID dynamic images of ten lung cancer patients, which included 183 frames (mean: 18.3 per patient). The 4D dose distributions during the treatment time were successfully obtained by applying the dose calculation algorithm to the simulated 4D “treatment” CT images. The mean±standard deviation (SD) of the percentage errors between the prescribed dose and the estimated dose at an isocenter for all cases was 3.25±4.43%. The maximum error for the ten cases was 14.67% (prescribed dose: 1.50Gy, estimated dose: 1.72Gy), and the minimum error was 0.00%. The proposed framework could be feasible for monitoring the 4D dose distribution and dose errors within a patient's body during treatment. | A computerized framework for monitoring four-dimensional dose distributions during stereotactic body radiation therapy using a portal dose image-based 2D/3D registration approach |
S0895611114002018 | The predictive accuracy of iNPH diagnoses could be increased using a combination of supplemental tests for iNPH. To evaluate the dynamic state of water displacement during the cardiac cycle in idiopathic normal pressure hydrocephalus (iNPH), we determined the change in water displacement using q-space analysis of diffusion magnetic resonance image. ECG-triggered single-shot diffusion echo planar imaging was used. Water displacement was obtained from the displacement probability profile calculated by Fourier transform of the signal decay fitted as a function of the reciprocal spatial vector q. Then maximum minus minimum displacement (delta-displacement), of all cardiac phase images was calculated. We assessed the delta-displacement in white matter in patients with iNPH and atrophic ventricular dilation (atrophic VD), and in healthy volunteers (control group). Delta-displacement in iNPH was significantly higher than those in the atrophic VD and control. This shows that water molecules of the white matter in iNPH are easily fluctuated by volume loading of the cranium during the cardiac cycle, due to the decrease in intracranial compliance. There was no significant correlation between delta-displacement and displacement. The delta-displacement and the displacement do not necessarily yield the same kind of information. Delta-displacement demonstrated to obtain biophysical information about fluctuation. This analysis may be helpful in the understanding physiology and pathological condition in iNPH and the assisting in the diagnosis. | Dynamic state of water molecular displacement of the brain during the cardiac cycle in idiopathic normal pressure hydrocephalus |
S089561111400202X | The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging. | An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test |
S0895611114002031 | In this paper, we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%. | Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation |
S0895611115000026 | Lipodystrophy is a pathological condition characterized by the focal or general absence of adipose tissue. Surgeons reset the patient's surface contours using injectable materials to recreate a normal physical appearance. However, due to difficulties in preoperative planning and intraoperative assessment, about 15% of the surgical procedures involved are reinterventions to improve volume or symmetry. This increases the need for an available, efficient tool capable of providing the surgeon with a good estimation of the volumes to be injected before the intervention proper begins. This work describes a virtual reality-based application for the surgical planning of facial lipodystrophy correction (FLIC). The tool uses points selected interactively by the surgeon to compute a curve that delimits the surface area to be operated on. It then automatically computes an estimated natural reconstructed surface and the quantity of volume that needs to be implanted during the intervention. Experiments have been carried out in which the filling volumes estimated using FLIC and ZBrush software were compared with the real volumes injected by the surgeon. ICCs higher than 0.97 indicate that there were no significant differences between the respective measurements, thus validating the tool proposed in this paper. | 3D surgical planning in patients affected by lipodystrophy |
S0895611115000038 | This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. | A multiscale decomposition approach to detect abnormal vasculature in the optic disc |
S089561111500004X | Colorectal cancer usually appears in polyps developed from the mucosa. Carcinoma is frequently found in those polyps larger than 10mm and therefore only this kind of polyps is sent for pathology examination. In consequence, accurate estimation of a polyp size determines the surveillance interval after polypectomy. The follow up consists in a periodic colonoscopy whose frequency depends on the estimation of the size polyp. Typically, this polyp measure is achieved by examining the lesion with a calibrated endoscopy tool. However, measurement is very challenging because it must be performed during a procedure subjected to a complex mix of noise sources, namely anatomical variability, drastic illumination changes and abrupt camera movements. This work introduces a semi-automatic method that estimates a polyp size by propagating an initial manual delineation in a single frame to the whole video sequence using a spatio-temporal characterization of the lesion, during a routine endoscopic examination. The proposed approach achieved a Dice Score of 0.7 in real endoscopy video-sequences, when comparing with an expert. In addition, the method obtained a root mean square error (RMSE) of 0.87mm in videos artificially captured in a cylindric structure with spheres of known size that simulated the polyps. Finally, in real endoscopy sequences, the diameter estimation was compared with measures obtained by a group of four experts with similar experience, obtaining a RMSE of 4.7mm for a set of polyps measuring from 5 to 20mm. An ANOVA test performed for the five groups of measurements (four experts and the method) showed no significant differences (p <0.01). | Estimating the size of polyps during actual endoscopy procedures using a spatio-temporal characterization |
S0895611115000051 | Watershed based modification of intelligent scissors has been developed. This approach requires a preprocessing phase with anisotropic diffusion to reduce subtle edges. Then, the watershed transform enhances the corridors. Finally, a roaming procedure, developed in this study, delineates the edge selected by a user. Due to a very restrictive set of pixels, subjected to the analysis, this approach significantly reduces the computational complexity. Moreover, the accuracy of the algorithm performance makes often one click point to be sufficient for one edge delineation. The method has been evaluated on structures as different in shape and appearance as the retina layers in OCT exams, chest and abdomen in CT and knee in MR studies. The accuracy is comparable with the traditional Life-Wire approach, whereas the analysis time decreases due to the reduction of the user interaction and number of pixels processed by the method. | Watershed based intelligent scissors |
S0895611115000063 | This study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84±0.11 for the epicardium and 0.79±0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82±0.14, which demonstrates the robustness of the algorithm. | Right ventricular segmentation in cardiac MRI with moving mesh correspondences |
S0895611115000075 | In the field of orthodontic planning, the creation of a complete digital dental model to simulate and predict treatments is of utmost importance. Nowadays, orthodontists use panoramic radiographs (PAN) and dental crown representations obtained by optical scanning. However, these data do not contain any 3D information regarding tooth root geometries. A reliable orthodontic treatment should instead take into account entire geometrical models of dental shapes in order to better predict tooth movements. This paper presents a methodology to create complete 3D patient dental anatomies by combining digital mouth models and panoramic radiographs. The modeling process is based on using crown surfaces, reconstructed by optical scanning, and root geometries, obtained by adapting anatomical CAD templates over patient specific information extracted from radiographic data. The radiographic process is virtually replicated on crown digital geometries through the Discrete Radon Transform (DRT). The resulting virtual PAN image is used to integrate the actual radiographic data and the digital mouth model. This procedure provides the root references on the 3D digital crown models, which guide a shape adjustment of the dental CAD templates. The entire geometrical models are finally created by merging dental crowns, captured by optical scanning, and root geometries, obtained from the CAD templates. | Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates |
S0895611115000087 | Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases. | Iterative mesh transformation for 3D segmentation of livers with cancers in CT images |
S0895611115000099 | An abdominal aortic aneurysm (AAA) is a pathological dilation of the abdominal aorta that may lead to a rupture with fatal consequences. Endovascular aneurysm repair (EVAR) is a minimally invasive surgical procedure consisting of the deployment and fixation of a stent–graft that isolates the damaged vessel wall from blood circulation. The technique requires adequate endovascular device sizing, which may be performed by vascular analysis and quantification on Computerized Tomography Angiography (CTA) scans. This paper presents a novel 3D CTA image-based software for AAA inspection and EVAR sizing, eVida Vascular, which allows fast and accurate 3D endograft sizing for standard and fenestrated endografts. We provide a description of the system and its innovations, including the underlying vascular image analysis and visualization technology, functional modules and user interaction. Furthermore, an experimental validation of the tool is described, assessing the degree of agreement with a commercial, clinically validated software, when comparing measurements obtained for standard endograft sizing in a group of 14 patients. | Standard and fenestrated endograft sizing in EVAR planning: Description and validation of a semi-automated 3D software |
S0895611115000105 | Motivation: Treatment of intracranial aneurysms with flow diverters (FDs) has recently become an attractive alternative. Although considerable effort has been devoted to understand their effects on the time-averaged or peak systolic flow field, no previous study has analyzed the variability of FD-induced flow reduction along the cardiac cycle. Methods: Fourteen saccular aneurysms, candidates for FD treatment because of their morphology, located on the internal carotid artery were virtually treated with FDs and pre- and post-treatment blood flow was simulated with CFD techniques. Common hemodynamic variables were recorded at each time step of the cardiac cycle and differences between the untreated and treated models were assessed. Results: Flow pulsatility, expressed by the pulsatility index (PI) of the velocity, significantly increased (36.0%; range: 14.6–88.3%) after FD treatment. Peak systole velocity reduction was significantly smaller (30.5%; range: 19.6–51.0%) than time-averaged velocity reduction (43.0%; range: 29.1–69.8%). No changes were observed in the aneurysmal pressure. Conclusions: FD-induced flow reduction varies considerably during the cardiac cycle. FD treatment significantly increased the flow pulsatility in the aneurysm. | Change in aneurysmal flow pulsatility after flow diverter treatment |
S0895611115000452 | Nowadays, in the era of common computerization, transmission and reflection methods are intensively developed in addition to improving classical ultrasound methods (US) for imaging of tissue structure, in particular ultrasound transmission tomography UTT (analogous to computed tomography CT which uses X-rays) and reflection tomography URT (based on the synthetic aperture method used in radar imaging techniques). This paper presents and analyses the results of ultrasound transmission tomography imaging of the internal structure of the female breast biopsy phantom CIRS Model 052A and the results of the ultrasound reflection tomography imaging of a wire sample. Imaging was performed using a multi-modal ultrasound computerized tomography system developed with the participation of a private investor. The results were compared with the results of imaging obtained using dual energy CT, MR mammography and conventional US method. The obtained results indicate that the developed UTT and URT methods, after the acceleration of the scanning process, thus enabling in vivo examination, may be successfully used for detection and detailed characterization of breast lesions in women. | Imaging results of multi-modal ultrasound computerized tomography system designed for breast diagnosis |
S0895611115000543 | The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. | Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography |
S0895611115000567 | We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WM-DOVA (Window Median Depth of Valleys Accumulation) energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians’ fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. | WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians |
S0895611115000579 | To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions. | Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach |
S0895611115000580 | Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses. | GPU accelerated dynamic functional connectivity analysis for functional MRI data |
S0895611115000592 | Coherent anti-Stokes Raman scattering (CARS) microscopy is a powerful tool for fast label-free tissue imaging, which is promising for early medical diagnostics. To facilitate the diagnostic process, automatic image analysis algorithms, which are capable of extracting relevant features from the image content, are needed. In this contribution we perform an automated classification of healthy and tumor areas in CARS images of basal cell carcinoma (BCC) skin samples. The classification is based on extraction of texture features from image regions and subsequent classification of these regions into healthy and cancerous with a perceptron algorithm. The developed approach is capable of an accurate classification of texture types with high sensitivity and specificity, which is an important step towards an automated tumor detection procedure. | Texture analysis and classification in coherent anti-Stokes Raman scattering (CARS) microscopy images for automated detection of skin cancer |
S0895611115000609 | Malignant melanoma causes the majority of deaths related to skin cancer. Nevertheless, it is the most treatable one, depending on its early diagnosis. The early prognosis is a challenging task for both clinicians and dermatologist, due to the characteristic similarities of melanoma with other skin lesions such as dysplastic nevi. In the past decades, several computerized lesion analysis algorithms have been proposed by the research community for detection of melanoma. These algorithms mostly focus on differentiating melanoma from benign lesions and few have considered the case of melanoma against dysplastic nevi. In this paper, we consider the most challenging task and propose an automatic framework for differentiation of melanoma from dysplastic nevi. The proposed framework also considers combination and comparison of several texture features beside the well used colour and shape features based on “ABCD” clinical rule in the literature. Focusing on dermoscopy images, we evaluate the performance of the framework using two feature extraction approaches, global and local (bag of words) and three classifiers such as support vector machine, gradient boosting and random forest. Our evaluation revealed the potential of texture features and random forest as an almost independent classifier. Using texture features and random forest for differentiation of melanoma and dysplastic nevi, the framework achieved the highest sensitivity of 98% and specificity of 70%. | Automatic differentiation of melanoma from dysplastic nevi |
S0895611115000610 | The main aim of this research is finding the feature vectors of the anterior and posterior cruciate ligaments (ACL and PCL). These feature vectors have to clearly define the ligaments structure and make it easier to diagnose them. Extraction of feature vectors is obtained by analysis of both anterior and posterior cruciate ligaments. This procedure is performed after the extraction process of both ligaments. In the first stage in order to reduce the area of analysis a region of interest including cruciate ligaments (CL) is outlined in order to reduce the area of analysis. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges has been implemented. After finding the region of interest (ROI), the fuzzy connectedness procedure is performed. This procedure permits to extract the anterior and posterior cruciate ligament structures. In the last stage, on the basis of the extracted anterior and posterior cruciate ligament structures, 3-dimensional models of the anterior and posterior cruciate ligament are built and the feature vectors created. This methodology has been implemented in MATLAB and tested on clinical T1-weighted magnetic resonance imaging (MRI) slices of the knee joint. The 3D display is based on the Visualization Toolkit (VTK). | Features extraction in anterior and posterior cruciate ligaments analysis |
S0895611115000622 | Detection of region specific voxel is a true challenge in many segmentation procedures. In this study a concept of implementing granular computing in the detection of anatomical structures in abdominal computed tomography (CT) scans is introduced. After proving the usefulness of the information granules to identify voxels that mark certain organs, an automatic model-based approach has been developed. A three-parameter granule that combines the interval and density distribution of voxels has been introduced and employed to identify organ specific voxels of the liver, spleen and kidneys. The specificity of the information granules varies between 90 and 99% for the liver and spleen and over 85% for the kidneys. | Granular computing in model based abdominal organs detection |
S0895611115000634 | Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. | Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy |
S0895611115000646 | Diabetic retinopathy is the major cause of blindness in the world. It has been shown that early diagnosis can play a major role in prevention of visual loss and blindness. This diagnosis can be made through regular screening and timely treatment. Besides, automation of this process can significantly reduce the work of ophthalmologists and alleviate inter and intra observer variability. This paper provides a fully automated diabetic retinopathy screening system with the ability of retinal image quality assessment. The novelty of the proposed method lies in the use of Morphological Component Analysis (MCA) algorithm to discriminate between normal and pathological retinal structures. To this end, first a pre-screening algorithm is used to assess the quality of retinal images. If the quality of the image is not satisfactory, it is examined by an ophthalmologist and must be recaptured if necessary. Otherwise, the image is processed for diabetic retinopathy detection. In this stage, normal and pathological structures of the retinal image are separated by MCA algorithm. Finally, the normal and abnormal retinal images are distinguished by statistical features of the retinal lesions. Our proposed system achieved 92.01% sensitivity and 95.45% specificity on the Messidor dataset which is a remarkable result in comparison with previous work. | Fully automated diabetic retinopathy screening using morphological component analysis |
S0895611115000658 | This paper presents a method for automatic color and intensity normalization of digitized histology slides stained with two different agents. In comparison to previous approaches, prior information on the stain vectors is used in the plane estimation process, resulting in improved stability of the estimates. Due to the prevalence of hematoxylin and eosin staining for histology slides, the proposed method has significant practical utility. In particular, it can be used as a first step to standardize appearance across slides and is effective at countering effects due to differing stain amounts and protocols and counteracting slide fading. The approach is validated against non-prior plane-fitting using synthetic experiments and 13 real datasets. Results of application of the method to adjustment of faded slides are given, and the effectiveness of the method in aiding statistical classification is shown. | Appearance normalization of histology slides |
S089561111500066X | The present research was directed to effective image restoration with the extraction of ischemic edema signs. Computerized support of hyperacute stroke diagnosis based on routinely used computerized tomography (CT) scans was optimized to visualize the infarct extent more precisely. In particular, a beneficial support of time-limited appropriate decision of whether to treat the patient by thrombolysis is expected. Because of a limited accuracy in determining the area of core infarction, particularly in the early hours of symptoms’ onset, a variational approach to sensed data recovery was applied. Proposed methodology adjusts fidelity norms and regularization priors integrated with simulated sensing procedures in a compressed sensing framework. Experimental study confirmed almost perfect recognition of ischemic stroke in a test set of over 500 CT scans. | Recovery of CT stroke hypodensity – An adaptive variational approach |
S0895611115000671 | We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects. We show that in many cases, use of the k-support norm leads to better predictive performance, solution stability, and interpretability as compared to other standard approaches. We additionally analyze the advantages of using the absolute loss function versus the standard squared loss which leads to significantly better predictive performance for the regularization methods tested in almost all cases. Our results support the use of the k-support norm for fMRI analysis and on the clinical side, the generalizability of the I-RISA model of cocaine addiction. | Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm |
S0895611115000683 | Multiplex immunohistochemistry (IHC) staining is a new, emerging technique for the detection of multiple biomarkers within a single tissue section. The initial key step in multiplex IHC image analysis in digital pathology is of tremendous clinical importance due to its ability to accurately unmix the IHC image and differentiate each of the stains. The technique has become popular due to its significant efficiency and the rich diagnostic information it contains. The intriguing task of unmixing a three-channel CCD color camera acquired RGB image into more than three colors is very challenging, and to the best of our knowledge, hardly studied in academic literature. This paper presents a novel stain unmixing algorithm for brightfield multiplex IHC images based on a group sparsity model. The proposed framework achieves robust unmixing for more than three chromogenic dyes while preserving the biological constraints of the biomarkers. Typically, a number of biomarkers co-localize in the same cell parts named priori. With this biological information in mind, the number of stains at one pixel therefore has a fixed up-bound, i.e. equivalent to the number of co-localized biomarkers. By leveraging the group sparsity model, the fractions of stain contributions from the co-localized biomarkers are explicitly modeled into one group to yield the least square solution within the group. A sparse solution is obtained among the groups since ideally only one group of biomarkers is present at each pixel. The algorithm is evaluated on both synthetic and clinical data sets, and demonstrates better unmixing results than the existing strategies. | Group sparsity model for stain unmixing in brightfield multiplex immunohistochemistry images |
S0895611115000774 | Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images. | Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis |
S0895611115000816 | Inverse consistency is an important feature for non-rigid image transformation in medical imaging analysis. In this paper, a simple and efficient inverse consistent image transformation estimation algorithm is proposed to preserve correspondence of landmarks and accelerate convergence. The proposed algorithm estimates both the forward and backward transformations simultaneously in the way that they are inverse to each other based on the correspondence of landmarks. Instead of computing the inverse functions and the inverse consistent transformations, respectively, we combine them together, which can improve computation efficiency significantly. Moreover, radial basis functions (RBFs) based transformation is adopted in our algorithm, which can handle deformation with local or global support. Our algorithm maps one landmark to its corresponding position exactly using the forward and backward transformations. Moreover, our algorithm is employed to estimate the forward and backward transformations in robust point matching, as well to demonstrate the application of our algorithm in image registration. The experiment results of uniform grids and test images indicate the improvement of the proposed algorithm in the aspect of inverse consistency of transformations and the reduction of the computation time of the forward and the backward transformations. The performance of our algorithm applying to robust point matching is evaluated using both brain slices and lung slices. Our experiments show that by combing robust point matching with our algorithm, the registration accuracy can be improved and the smoothness of transformations can be preserved. | A fast algorithm to estimate inverse consistent image transformation based on corresponding landmarks |
S089561111500083X | We propose a novel 3D–2D registration approach for micro-computed tomography (μCT) and histology (HI), constructed for dental implant biopsies, that finds the position and normal vector of the oblique slice from μCT that corresponds to HI. During image pre-processing, the implants and the bone tissue are segmented using a combination of thresholding, morphological filters and component labeling. After this, chamfer matching is employed to register the implant edges and fine registration of the bone tissues is achieved using simulated annealing. The method was tested on n =10 biopsies, obtained at 20 weeks after non-submerged healing in the canine mandible. The specimens were scanned with μCT 100 and processed for hard tissue sectioning. After registration, we assessed the agreement of bone to implant contact (BIC) using automated and manual measurements. Statistical analysis was conducted to test the agreement of the BIC measurements in the registered samples. Registration was successful for all specimens and agreement of the respective binary images was high (median: 0.90, 1.–3. Qu.: 0.89–0.91). Direct comparison of BIC yielded that automated (median 0.82, 1.–3. Qu.: 0.75–0.85) and manual (median 0.61, 1.–3. Qu.: 0.52–0.67) measures from μCT were significant positively correlated with HI (median 0.65, 1.–3. Qu.: 0.59–0.72) between μCT and HI groups (manual: R 2 =0.87, automated: R 2 =0.75, p <0.001). The results show that this method yields promising results and that μCT may become a valid alternative to assess osseointegration in three dimensions. | Automated 3D–2D registration of X-ray microcomputed tomography with histological sections for dental implants in bone using chamfer matching and simulated annealing |
S0895611115000841 | Several recent studies have demonstrated the potential for quantitative imaging features to classify non-small cell lung cancer (NSCLC) patients as high or low risk. However applying the results from one institution to another has been difficult because of the variations in imaging techniques and feature measurement. Our study was designed to determine the effect of some of these sources of uncertainty on image features extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC) tumors. CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current). Texture values did not vary substantially with the choice of breathing phase; however, almost half (12 out of 28) of the measured textures did change significantly when measured from the average images compared to the end-of-exhale phase. Of the 28 features, 8 showed a significant variation when measured from the largest cross sectional slice compared to the entire tumor, but 14 were correlated to the entire tumor value. While simulating a decrease in tube voltage had a negligible impact on texture features, simulating a decrease in mA resulted in significant changes for 13 of the 23 texture values. Our results suggest that substantial variation exists when textures are measured under different conditions, and thus the development of a texture analysis standard would be beneficial for comparing features between patients and institutions. | Preliminary investigation into sources of uncertainty in quantitative imaging features |
S0895611115000853 | Recently, several pattern recognition methods have been proposed to automatically discriminate between patients with and without Alzheimer's disease using different imaging modalities: sMRI, fMRI, PET and SPECT. Classical approaches in visual information retrieval have been successfully used for analysis of structural MRI brain images. In this paper, we use the visual indexing framework and pattern recognition analysis based on structural MRI data to discriminate three classes of subjects: normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). The approach uses the circular harmonic functions (CHFs) to extract local features from the most involved areas in the disease: hippocampus and posterior cingulate cortex (PCC) in each slice in all three brain projections. The features are quantized using the Bag-of-Visual-Words approach to build one signature by brain (subject). This yields a transformation of a full 3D image of brain ROIs into a 1D signature, a histogram of quantized features. To reduce the dimensionality of the signature, we use the PCA technique. Support vector machines classifiers are then applied to classify groups. The experiments were conducted on a subset of ADNI dataset and applied to the “Bordeaux-3City” dataset. The results showed that our approach achieves respectively for ADNI dataset and “Bordeaux-3City” dataset; for AD vs NC classification, an accuracy of 83.77% and 78%, a specificity of 88.2% and 80.4% and a sensitivity of 79.09% and 74.7%. For NC vs MCI classification we achieved for the ADNI datasets an accuracy of 69.45%, a specificity of 74.8% and a sensitivity of 62.52%. For the most challenging classification task (AD vs MCI), we reached an accuracy of 62.07%, a specificity of 75.15% and a sensitivity of 49.02%. The use of PCC visual features description improves classification results by more than 5% compared to the use of hippocampus features only. Our approach is automatic, less time-consuming and does not require the intervention of the clinician during the disease diagnosis. | Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex |
S0895611115000865 | Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods. | Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques |
S0895611115000877 | Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R =0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07±1.00mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation. | Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation |
S0895611115000889 | This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. | Classification of multiple sclerosis lesions using adaptive dictionary learning |
S0895611115000890 | Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain. | Tissue-specific sparse deconvolution for brain CT perfusion |
S0895611115000907 | Accurate registration of dynamic contrast-enhanced (DCE) MR breast images is challenging due to the temporal variations of image intensity and the non-rigidity of breast motion. The former can cause the well-known tumor shrinking/expanding problem in registration process while the latter complicates the task by requiring an estimation of non-rigid deformation. In this paper, we treat the intensity's temporal variations as “corruptions” which spatially distribute in a sparse pattern and model them with a L 1 norm and a Lorentzian norm. We show that these new image similarity measurements can characterize the non-Gaussian property of the difference between the pre-contrast and post-contrast images and help to resolve the shrinking/expanding problem by forgiving significant image variations. Furthermore, we propose an iteratively re-weighted least squares based method and a linear programming based technique for optimizing the objective functions obtained using these two novel norms. We show that these optimization techniques outperform the traditional gradient-descent approach. Experimental results with sequential DCE-MR images from 28 patients show the superior performances of our algorithms. | Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images |
S0895611115000919 | This paper considers the problem of the CT based quantitative assessment of the craniosynostosis before and after the surgery. First, fast and efficient brain segmentation approach is proposed. The algorithm is robust to discontinuity of skull. As a result it can be applied both in pre- and post-operative cases. Additionally, image processing and analysis algorithms are proposed for describing the disease based on CT scans. The proposed algorithms automate determination of the standard linear indices used for assessment of the craniosynostosis (i.e. cephalic index CI and head circumference HC) and allow for planar and volumetric analysis which so far have not been reported. Results of applying the introduced methods to sample craniosynostotic cases before and after the surgery are presented and discussed. The results show that the proposed brain segmentation algorithm is characterized by high accuracy when applied both in the pre- and postoperative craniosynostosis, while the introduced planar and volumetric indices for the disease description may be helpful to distinguish between the types of the disease. | The quantitative assessment of the pre- and postoperative craniosynostosis using the methods of image analysis |
S0895611115000920 | The analysis of the vestibular system (VS) is an important research topic in medical image analysis. VS is a sensory structure in the inner ear for the perception of spatial orientation. It is believed several diseases, such as the Adolescent Idiopathic Scoliosis (AIS), are due to the impairment of the VS function. The morphology of the VS is thus of great research significance. A major challenge is that the VS is a genus-3 surface. The high-genus topology of the VS poses great challenges to find accurate pointwise correspondences between the surfaces and whereby perform accurate shape analysis. In this paper, we present a method to obtain the landmark constrained diffeomorphic registration between the VS surfaces based on the quasi-conformal theory. Given a set of corresponding landmarks on the VS surfaces, a diffeomorphism between the VS surfaces that matches the features consistently can be obtained. The basic idea is to iteratively search for an admissible Beltrami coefficient, which is associated to our desired landmark matching registration. With the obtained surface registrations, vertex-wise morphometric analysis can be carried out. Two types of geometric features are used for shape comparison. One is the collection of homotopic loops on each canals of the VS, which can be used to measure the local thickness of the canals. From the homotopic loops, centerlines can be extracted. By examining the deviations of the centerlines from the best fit planes, bendings of the canals can be detected. The second geometric feature is the minimal surface enclosed by the homotopic loop. From the minimal surfaces of each homotopic loops, cross-sectional area of the canals can be evaluated. To study the local shape difference more comprehensively, a complete shape index, which is defined using the Beltrami coefficients and surface curvatures, is used. We test proposed registration method on 15 VS of normal control subjects and 12 VS of patients suffering from AIS. Experimental results show the efficacy and accuracy of the proposed algorithm to compute the VS surface registration. Shape analysis has also been carried out using the proposed geometric features and shape index, which reveals shape differences in the posterior canal between normal and diseased AIS groups. | Landmark constrained registration of high-genus surfaces applied to vestibular system morphometry |
S0895611115000932 | The paper presents the automatic computer-aided balance assessment system for supporting and monitoring the diagnosis and rehabilitation process of patients with limited mobility or disabled in home environment. The system has adopted seven Berg Balance Scale activities. The assessment approach is based on the accelerometric signals acquired by the inertial sensors worn by the patient. Several specific, mostly medium frequency features of signals are introduced and discussed. The reduction of the feature vector has been performed using the multilevel Fisher's linear discriminant. The classification employs the multilayer perceptron artificial neural network. The direct assessment effectiveness ranges from 75% to 94% for various activities. | Accelerometric signals in automatic balance assessment |
S0895611115000944 | Cerebrospinal fluid imaging plays a significant role in the clinical diagnosis of brain disorders, such as hydrocephalus and Alzheimer's disease. While three-dimensional images of cerebrospinal fluid are very detailed, the complex structures they contain can be time-consuming and laborious to interpret. This paper presents a simple technique that represents the intracranial cerebrospinal fluid distribution as a two-dimensional image in such a way that the total fluid volume is preserved. We call this a volumetric relief map, and show its effectiveness in a characterization and analysis of fluid distributions and networks in hydrocephalus patients and healthy adults. | Volumetric relief map for intracranial cerebrospinal fluid distribution analysis |
S0895611115000956 | In this paper a novel multi-stage automatic method for brain tumour detection and neovasculature assessment is presented. First, the brain symmetry is exploited to register the magnetic resonance (MR) series analysed. Then, the intracranial structures are found and the region of interest (ROI) is constrained within them to tumour and peritumoural areas using the Fluid Light Attenuation Inversion Recovery (FLAIR) series. Next, the contrast-enhanced lesions are detected on the basis of T1-weighted (T1W) differential images before and after contrast medium administration. Finally, their vascularisation is assessed based on the Regional Cerebral Blood Volume (RCBV) perfusion maps. The relative RCBV (rRCBV) map is calculated in relation to a healthy white matter, also found automatically, and visualised on the analysed series. Three main types of brain tumours, i.e. HG gliomas, metastases and meningiomas have been subjected to the analysis. The results of contrast enhanced lesions detection have been compared with manual delineations performed independently by two experts, yielding 64.84% sensitivity, 99.89% specificity and 71.83% Dice Similarity Coefficient (DSC) for twenty analysed studies of subjects with brain tumours diagnosed. | Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis |
S089561111500097X | Diabetes increases the risk of developing any deterioration in the blood vessels that supply the retina, an ailment known as Diabetic Retinopathy (DR). Since this disease is asymptomatic, it can only be diagnosed by an ophthalmologist. However, the growth of the number of ophthalmologists is lower than the growth of the population with diabetes so that preventive and early diagnosis is difficult due to the lack of opportunity in terms of time and cost. Preliminary, affordable and accessible ophthalmological diagnosis will give the opportunity to perform routine preventive examinations, indicating the need to consult an ophthalmologist during a stage of non proliferation. During this stage, there is a lesion on the retina known as microaneurysm (MA), which is one of the first clinically observable lesions that indicate the disease. In recent years, different image processing algorithms, which allow the detection of the DR, have been developed; however, the issue is still open since acceptable levels of sensitivity and specificity have not yet been reached, preventing its use as a pre-diagnostic tool. Consequently, this work proposes a new approach for MA detection based on (1) reduction of non-uniform illumination; (2) normalization of image grayscale content to improve dependence of images from different contexts; (3) application of the bottom-hat transform to leave reddish regions intact while suppressing bright objects; (4) binarization of the image of interest with the result that objects corresponding to MAs, blood vessels, and other reddish objects (Regions of Interest—ROIs) are completely separated from the background; (5) application of the hit-or-miss Transformation on the binary image to remove blood vessels from the ROIs; (6) two features are extracted from a candidate to distinguish real MAs from FPs, where one feature discriminates round shaped candidates (MAs) from elongated shaped ones (vessels) through application of Principal Component Analysis (PCA); (7) the second feature is a count of the number of times that the radon transform of the candidate ROI, evaluated at the set of discrete angle values {0°, 1°, 2°, …, 180°}, is characterized by a valley between two peaks. The proposed approach is tested on the public databases DiaretDB1 and Retinopathy Online Challenge (ROC) competition. The proposed MA detection method achieves sensitivity, specificity and precision of 92.32%, 93.87% and 95.93% for the diaretDB1 database and 88.06%, 97.47% and 92.19% for the ROC database. Theory, results, challenges and performance related to the proposed MA detecting method are presented. | A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images |
S0895611115000993 | An efficient iterative algorithm, based on recent work in non-convex optimization and generalized p-shrinkage mappings, is proposed for volume image reconstruction from circular cone-beam scans. Conventional total variation regularization makes use of L1 norm of gradient magnitude images (GMI). However, this paper utilizes a generalized penalty function, induced by p-shrinkage, of GMI which is proven to be a better measurement of its sparsity. The reconstruction model is formed using generalized total p-variation (TpV) minimization, which differs with the state of the art methods, with the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. Theoretically, the proximal mapping for penalty functions induced by p-shrinkage has an exact and closed-form expression; thus, the constrained optimization can be stably and efficiently solved by the alternating direction minimization (ADM) scheme. Each sub-problem decoupled by variable splitting is minimized by explicit and easy-to-implement formulas developed by ADM. The proposed algorithm is efficiently implemented using a graphics processing unit and is referred to as “TpV-ADM.” This method is robust and accurate even for very few view reconstruction datasets. Verifications and comparisons performed using various datasets (including ideal, noisy, and real projections) illustrate that the proposed method is effective and promising. | Efficient TpV minimization for circular, cone-beam computed tomography reconstruction via non-convex optimization |
S0895611115001007 | To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n =38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation. | Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT |
S0895611115001019 | In dentistry, clinical radiographs (also called X-ray images) reflect the intensity loss of an X-ray when being transmitted through the mandibular objects, and this loss is quantified in terms of grey values. While such images are standardly used for pathology detection by the experienced dentist, we here present a new method for getting more quantitative information out of such 2D radiographs, “extending” them into the third dimension. This “extension” requires consistent combination of X-ray physics (namely, X-ray intensity loss quantification along paths orthogonal to the panoramic clinical image and X-ray attenuation averaging for composite materials) with anatomically known upper and lower limits of vascular porosities in cortical and trabecular bone compartments. Correspondingly computed ranges of overall organ thicknesses are extremely narrow, suggesting adequate estimation of thickness characteristics from 2D radiographic panoramas used clinically, while predicted cortical and trabecular thickness ranges vary by ±8.47% and ±16.13%, respectively. The proposed method also identifies variations between thicknesses at similar anatomical locations left and right of the face's symmetry axis, and molar regions turn out to be thicker than those close to incisors. This paves the way to more detailed diagnostic activities, e.g. in combination with Finite Element simulations. | X-ray physics- and bone composition-based estimation of thickness characteristics from clinical mandibular radiographs |
S0895611115001020 | Segmenting the lesion areas from ultrasound (US) images is an important step in the intra-operative planning of high-intensity focused ultrasound (HIFU). However, accurate segmentation remains a challenge due to intensity inhomogeneity, blurry boundaries in HIFU US images and the deformation of uterine fibroids caused by patient's breathing or external force. This paper presents a novel dynamic statistical shape model (SSM)-based segmentation method to accurately and efficiently segment the target region in HIFU US images of uterine fibroids. For accurately learning the prior shape information of lesion boundary fluctuations in the training set, the dynamic properties of stochastic differential equation and Fokker–Planck equation are incorporated into SSM (referred to as SF-SSM). Then, a new observation model of lesion areas (named to RPFM) in HIFU US images is developed to describe the features of the lesion areas and provide a likelihood probability to the prior shape given by SF-SSM. SF-SSM and RPFM are integrated into active contour model to improve the accuracy and robustness of segmentation in HIFU US images. We compare the proposed method with four well-known US segmentation methods to demonstrate its superiority. The experimental results in clinical HIFU US images validate the high accuracy and robustness of our approach, even when the quality of the images is unsatisfactory, indicating its potential for practical application in HIFU therapy. | Segmentation of uterine fibroid ultrasound images using a dynamic statistical shape model in HIFU therapy |
S0895611115001032 | The tongue is a critical organ for a variety of functions, including swallowing, respiration, and speech. It contains intrinsic and extrinsic muscles that play an important role in changing its shape and position. Diffusion tensor imaging (DTI) has been used to reconstruct tongue muscle fiber tracts. However, previous studies have been unable to reconstruct the crossing fibers that occur where the tongue muscles interdigitate, which is a large percentage of the tongue volume. To resolve crossing fibers, multi-tensor models on DTI and more advanced imaging modalities, such as high angular resolution diffusion imaging (HARDI) and diffusion spectrum imaging (DSI), have been proposed. However, because of the involuntary nature of swallowing, there is insufficient time to acquire a sufficient number of diffusion gradient directions to resolve crossing fibers while the in vivo tongue is in a fixed position. In this work, we address the challenge of distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging by using a multi-tensor model with a fixed tensor basis and incorporating prior directional knowledge. The prior directional knowledge provides information on likely fiber directions at each voxel, and is computed with anatomical knowledge of tongue muscles. The fiber directions are estimated within a maximum a posteriori (MAP) framework, and the resulting objective function is solved using a noise-aware weighted ℓ1-norm minimization algorithm. Experiments were performed on a digital crossing phantom and in vivo tongue diffusion data including three control subjects and four patients with glossectomies. On the digital phantom, effects of parameters, noise, and prior direction accuracy were studied, and parameter settings for real data were determined. The results on the in vivo data demonstrate that the proposed method is able to resolve interdigitated tongue muscles with limited gradient directions. The distributions of the computed fiber directions in both the controls and the patients were also compared, suggesting a potential clinical use for this imaging and image analysis methodology. | A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging |
S0895611115001044 | This paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter parameters are derived from a smaller set of image features that we call keypoints. A Gabor filter bank, parameterised empirically by ROC analysis, is used to extract keypoints representing significant scale specific vessel features using an approach inspired by the SIFT algorithm. We first determine keypoints using a validation set and then derive seeds from these points to initialise a k-means clustering algorithm which builds a texton dictionary from another training set. During testing we use a simple 1-NN classifier to identify vessel/non-vessel pixels and evaluate our system using the DRIVE database. We achieve average values of sensitivity, specificity and accuracy of 78.12%, 96.68% and 95.05%, respectively. We find that clusters of filter responses from keypoints are more robust than those derived from hand-labelled pixels. This, in turn yields textons more representative of vessel/non-vessel classes and mitigates problems arising due to intra and inter-observer variability. | Retinal vessel segmentation using multi-scale textons derived from keypoints |
S0895611115001056 | We propose a new deformable slice-to-volume registration method to register a 2D Transvaginal Ultrasound (TVUS) to a 3D Magnetic Resonance (MR) volume. Our main goal is to find a cross-section of the MR volume such that the endometrial implants and their depth of infiltration can be mapped from TVUS to MR. The proposed TVUS-MR registration method uses contour to surface correspondences through a novel variational one-step deformable Iterative Closest Point (ICP) method. Specifically, we find a smooth deformation field while establishing point correspondences automatically. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on both semi-synthetic and clinical data. To generate semi-synthetic data sets, 3D surfaces are deformed with 4–40% degrees of deformation and then various intersection curves are obtained at 0–20° cutting angles. Results show an average mean square error of 5.7934±0.4615mm, average Hausdorff distance of 2.493±0.14mm, and average Dice similarity coefficient of 0.9750±0.0030. | Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images |
S0895611115001068 | Intravascular ultrasound (IVUS) imaging is extremely important for detection and characterization of high-risk atherosclerotic plaques as well as gastrointestinal diseases. Recently, intravascular photoacoustic (IVPA) imaging has been used to differentiate the composition of biological tissues with high optical contrast and ultrasonic resolution. The combination of these imaging techniques could provide morphological information and molecular screening to characterize abnormal tissues, which would help physicians to ensure vital therapeutic value and prognostic significance for patients before commencing therapy. In this study, integration of a high-frequency IVUS imaging catheter (45MHz, single-element, unfocused, 0.7mm in diameter) with a multi-mode optical fiber (0.6mm in core diameter, 0.22 NA), an integrated intravascular ultrasonic–photoacoustic (IVUP) imaging catheter, was developed to provide spatial and functional information on light distribution in a turbid sample. Simultaneously, IVUS imaging was co-registered to IVPA imaging to construct 3D volumetric sample images. In a phantom study, a polyvinyl alcohol (PVA) tissue-mimicking arterial vessel phantom with indocyanine green (ICG) and methylene blue (MB) inclusion was used to demonstrate the feasibility of mapping the biological dyes, which are used in cardiovascular and cancer diagnostics. For the ex vivo study, an excised sample of pig intestine with ICG was utilized to target the biomarkers present in the gastrointestinal tumors or the atherosclerotic plaques with the proposed hybrid technique. The results indicated that IVUP endoscope with the 2.2-mm diameter catheter could be a useful tool for medical imaging. | Intravascular ultrasonic–photoacoustic (IVUP) endoscope with 2.2-mm diameter catheter for medical imaging |
S089561111500107X | Mitral valve (MV) diseases are among the most common types of heart diseases, while heart diseases are the most common cause of death worldwide. MV repair surgery is connected to higher survival rates and fewer complications than the total replacement of the MV, but MV repair requires extensive patient-specific therapy planning. The simulation of MV repair with a patient-specific model could help to optimize surgery results and make MV repair available to more patients. However, current patient-specific simulations are difficult to transfer to clinical application because of time-constraints or prohibitive requirements on the resolution of the image data. As one possible solution to the problem of patient-specific MV modeling, we present a mass-spring MV model based on 3D transesophageal echocardiographic (TEE) images already routinely acquired for MV repair therapy planning. Our novel approach to the rest-length estimation of springs allows us to model the global support of the MV leaflets through the chordae tendinae without the need for high-resolution image data. The model is used to simulate MV annuloplasty for five patients undergoing MV repair, and the simulated results are compared to post-surgical TEE images. The comparison shows that our model is able to provide a qualitative estimate of annuloplasty surgery. In addition, the data suggests that the model might also be applied to simulating the implantation of artificial chordae. | Mass-spring systems for simulating mitral valve repair using 3D ultrasound images |
S0895611115001160 | Fiber tractography techniques in diffusion magnetic resonance imaging have become a primary tool for studying the fiber architecture of biological tissues both noninvasively and in vivo. Streamline tracking, as a simple and efficient tractography technique, is widely used to reconstruct fiber pathways. It is however very sensitive to noisy estimation of local fiber orientations. In this paper, we propose a bundle constrained streamline method to accurately reconstruct multifiber pathways. The method introduces neighboring fiber consistency constraint in the tracking process and reconstructs fiber pathways that have optimal tradeoff between consistency with local fiber orientation estimations and similarity with neighboring fiber segment orientations. Results on synthetic, physical phantom and real human brain DW images show that the proposed method allows regular fiber pathways to be reconstructed and outperforms existing techniques. | Multifiber pathway reconstruction using bundle constrained streamline |
S0895611115001172 | Radiological assessment of treatment effectiveness of guided bone regeneration (GBR) method in postresectal and postcystal bone loss cases, observed for one year. Group of 25 patients (17 females and 8 males) who underwent root resection with cystectomy were evaluated. The following combination therapy of intraosseous deficits was used, consisting of bone augmentation with xenogenic material together with covering regenerative membranes and tight wound closure. The bone regeneration process was estimated, comparing the images taken on the day of the surgery and 12 months later, by means of Kodak RVG 6100 digital radiography set. The interpretation of the radiovisiographic image depends on the evaluation ability of the eye looking at it, which leaves a large margin of uncertainty. So, several texture analysis techniques were developed and used sequentially on the radiographic image. For each method, the results were the mean from the 25 images. These methods compute the fractal dimension (D), each one having its own theoretic basis. We used five techniques for calculating fractal dimension: power spectral density method, triangular prism surface area method, blanket method, intensity difference scaling method and variogram analysis. Our study showed a decrease of fractal dimension during the healing process after bone loss. We also found evidence that various methods of calculating fractal dimension give different results. During the healing process after bone loss, the surfaces of radiographic images became smooth. The result obtained show that our findings may be of great importance for diagnostic purpose. | Fractal texture analysis of the healing process after bone loss |
S0895611115001184 | Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n =19) and test (n =191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN+PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J =59.5±14.6 and Rand Ri =62.0±7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: J BN =35.2±24.9, O BN =49.6±32, J PCa =49.5±18.5, O PCa =72.7±14.8 and Ri =60.6±7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area. | Machine learning approaches to analyze histological images of tissues from radical prostatectomies |
S0895611115001196 | Purpose Assessing the treated region with locoregional therapy (LT) provides valuable information for predicting hepatocellular carcinoma (HCC) recurrence. The commonly used of assessment method is inefficient because it only compares two-dimensional CT images manually. In our previous work, we automatically aligned the two CT volumes to evaluate the therapeutic efficiency using registration algorithms. The non-rigid registration is applied to capture local deformation, however, it usually destroys internal structure. Taking these into consideration, this paper proposes a novel non-rigid registration approach for evaluating LT of HCC to maintain the image integrity. Method In our registration algorithm, a global affine transformation combined with localized cubic B-spline is used to estimate the significant non-rigid motions of two livers. The proposed method extends a classical non-rigid registration based on mutual information (MI) that uses an anatomical structure term to constrain the local deformation. The energy function can be defined based on the total one associated with the anatomical structure and deformation information. Optimal transformation is obtained by finding the equilibrium state in which the total energy is minimized, indicating that the anatomical landmarks have found their correspondences. Thus, we can use the same transformation to automatically transform the ablative region to the optimal position. Results Registration accuracy is evaluated using the clinical data. Improved results are obtained with respect to all criteria in our proposed method (MI-LC) than those in the MI-based non-rigid registration. The landmark distance error (LDE) of MI-LC is decreased by an average of 3.93mm compared to the case of MI-based registration. Moreover, it could be found regardless of how many landmarks applied in our proposed method, a significant reduction in LDE values using registrations based on MI-LC compared with those based on MI is confirmed. Conclusion Our proposed approach can guarantee the continuity, the accuracy and the smoothness of structures by constraining the anatomical features. The results clearly indicate that our method can retain the local deformation of the image. In addition, it assures the anatomical structure stability. | Non-rigid image registration with anatomical structure constraint for assessing locoregional therapy of hepatocellular carcinoma |
S0895611115001202 | The aim of this study was to analyze the usage and the data recorded by a RIS-PACS-connected contrast medium (CM) monitoring system (Certegra®, Bayer Healthcare, Leverkusen, Germany) over 19 months of CT activity. The system used was connected to two dual syringe power injectors (each associated with a 16-row and a high definition 64-row multidetector CT scanner, respectively), allowing to manage contrast medium injection parameters and to send and retrieve CT study-related information via RIS/PACS for any scheduled contrast-enhanced CT examination. The system can handle up to 64 variables and can be accessed via touchscreen by CT operators as well as via a web interface by registered users with three different hierarchy levels. Data related to CM injection parameters (i.e. iodine concentration, volume and flow rate of CM, iodine delivery rate and iodine dose, CM injection pressure, and volume and flow rate of saline), patient weight and height, and type of CT study over a testing period spanning from 1 June 2013 to 10 January 2015 were retrieved from the system. Technical alerts occurred for each injection event (such as system disarm due to technical failure, disarm due to operator's stop, incomplete filling of patient data fields, or excessively high injection pressure), as well as interoperability issues related to data sending and receiving to/from the RIS/PACS were also recorded. During the testing period, the CM monitoring system generated a total of 8609 reports, of which 7629 relative to successful injection events (88.6%). 331 alerts were generated, of which 40 resulted in injection interruption and 291 in CM flow rate limitation due to excessively high injection pressure (>325psi). Average CM volume and flow rate were 93.73±17.58mL and 3.53±0.89mL/s, and contrast injection pressure ranged between 5 and 167psi. A statistically significant correlation was found between iodine concentration and peak IDR (r s =0.2744, p <0.0001), as well as between iodine concentration and iodine dose (r s =0.3862, p <0.0001) for all CT studies. Automated contrast management systems can provide a full report of contrast use with the possibility to systematically compare different contrast injection protocols, minimize errors, and optimize organ-specific contrast enhancement for any given patient and clinical application. This can be useful to improve and harmonize the quality and consistency of contrast CT procedures within the same radiological department and across the hospital, as well as to monitor potential adverse events and overall costs. | Automated contrast medium monitoring system for computed tomography – Intra-institutional audit |
S0895611115001214 | White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice. | Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field |
S0895611115001238 | Multiparametric (mp)-Magnetic Resonance Imaging (MRI) is emerging as a powerful test to diagnose and stage prostate cancer (PCa). However, its interpretation is a time consuming and complex feat requiring dedicated radiologists. Computer-aided diagnosis (CAD) tools could allow better integration of data deriving from the different MRI sequences in order to obtain accurate, reproducible, non-operator dependent information useful to identify and stage PCa. In this paper, we present a fully automatic CAD system conceived as a 2-stage process. First, a malignancy probability map for all voxels within the prostate is created. Then, a candidate segmentation step is performed to highlight suspected areas, thus evaluating both the sensitivity and the number of false positive (FP) regions detected by the system. Training and testing of the CAD scheme is performed using whole-mount histological sections as the reference standard. On a cohort of 56 patients (i.e. 65 lesions) the area under the ROC curve obtained during the voxel-wise step was 0.91, while, in the second step, a per-patient sensitivity of 97% was reached, with a median number of FP equal to 3 in the whole prostate. The system here proposed could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologist's reporting time and the inter-reader variability. As an innovative setup, it could also be used to help the radiologist in setting the MRI-guided biopsy target. | A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging |
S089561111500124X | Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy. | Robust anatomical landmark detection with application to MR brain image registration |
S0895611115001251 | Purpose Texture patterns of hepatic fibrosis are one of the important biomarkers to diagnose and classify chronic liver disease from initial to end stage on computed tomography (CT) or magnetic resonance (MR) images. Computer-aided diagnosis (CAD) of liver cirrhosis using texture features has become popular in recent research advances. To date, however, properly selecting effective texture features and image parameters is still mostly undetermined and not well-defined. In this study, different types of datasets acquired from CT and MR images are investigated to select the optimal parameters and features for the proper classification of fibrosis. Methods A total of 149 patients were scanned by multi-detector computed tomography (MDCT) and 218 patients were scanned using 1.5T and 3T superconducting MR scanners for an abdominal examination. All cases were verified by needle biopsies as the gold standard of our experiment, ranging from 0 (no fibrosis) to 5 (cirrhosis). For each case, at least four sequenced phase images are acquired by CT or MR scanners: pre-contrast, arterial, portal venous and equilibrium phase. For both imaging modalities, 15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver as one set of input vectors. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: non-cirrhosis or cirrhosis. In addition, 10 ROIs in the liver are manually selected in a disperse manner by experienced radiologist from each sequenced image and each of the 15 features are averaged across the 10 ROIs for each case to reduce the validation time. The number of input items is selected from the various combinations of 15 features, from which the accuracy rate (AR) is calculated by counting the percentage of correct answers on each combination of features aggregated to determine a liver stage score and then compared to the gold standard. Results According to the accuracy rate (AR) calculated from each combination, the optimal number of texture features to classify liver fibrosis degree ranges from 4 to 7, no matter which modality was utilized. The overall performance calculated by the average sum of maximum AR value of all 15 features is 66.83% in CT images, while 68.14%, and 71.98% in MR images, respectively; among the 15 texture features, mean gray value and entropy are the most commonly used features in all 3 imaging datasets. The correlation feature has the lowest AR value and was removed as an effective feature in all datasets. AR value tends to increase with the injection of contrast agency, and both CT and MR images reach the highest AR performance during the equilibrium phase. Conclusions Comparing the accuracy of classification with two imaging modalities, the MR images have an advantage over CT images with regards to AR performance of the 15 selected texture features, while 3T MRI is better than 1.5T MRI to classify liver fibrosis. Finally, the texture analysis is more effective during equilibrium phase than in any of the other phased images. | Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging? |
S0895611115001263 | Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10–20μm. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11mm2 compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features. | A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning |
S0895611115001275 | Machine translation is evolving quite rapidly in terms of quality. Nowadays, we have several machine translation systems available in the web, which provide reasonable translations. However, these systems are not perfect, and their quality may decrease in some specific domains. This paper examines the effects of different training methods when it comes to Polish–English Statistical Machine Translation system used for the medical data. Numerous elements of the EMEA parallel text corpora and not related OPUS Open Subtitles project were used as the ground for creation of phrase tables and different language models including the development, tuning and testing of these translation systems. The BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the results of various systems. Our experiments deal with the systems that include POS tagging, factored phrase models, hierarchical models, syntactic taggers, and other alignment methods. We also executed a deep analysis of Polish data as preparatory work before automatized data processing such as true casing or punctuation normalization phase. Normalized metrics was used to compare results. Scores lower than 15% mean that Machine Translation engine is unable to provide satisfying quality, scores greater than 30% mean that translations should be understandable without problems and scores over 50 reflect adequate translations. The average results of Polish to English translations scores for BLEU, NIST, METEOR, and TER were relatively high and ranged from 7058 to 8272. The lowest score was 6438. The average results ranges for English to Polish translations were little lower (6758–7897). The real-life implementations of presented high quality Machine Translation Systems are anticipated in general medical practice and telemedicine. | Telemedicine as a special case of machine translation |
S0895611115001408 | Purpose MRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients’ progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. Method An imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists’ contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. Summary The MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record. | Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients |
S089561111500141X | A variety of vision ailments are indicated by anomalies in the choroid layer of the posterior visual section. Consequently, choroidal thickness and volume measurements, usually performed by experts based on optical coherence tomography (OCT) images, have assumed diagnostic significance. Now, to save precious expert time, it has become imperative to develop automated methods. To this end, one requires choroid outer boundary (COB) detection as a crucial step, where difficulty arises as the COB divides the choroidal granularity and the scleral uniformity only notionally, without marked brightness variation. In this backdrop, we measure the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimate the COB by thresholding. Subsequently, smooth COB estimates, mimicking manual delineation, are obtained using tensor voting. On five datasets, each consisting of 97 adult OCT B-scans, automated and manual segmentation results agree visually. We also demonstrate close statistical match (greater than 99.6% correlation) between choroidal thickness distributions obtained algorithmically and manually. Further, quantitative superiority of our method is established over existing results by respective factors of 27.67% and 76.04% in two quotient measures defined relative to observer repeatability. Finally, automated choroidal volume estimation, being attempted for the first time, also yields results in close agreement with that of manual methods. | Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section |
S0895611115001421 | Susceptibility-weighted imaging (SWI) is recognized as the preferred MRI technique for visualizing cerebral vasculature and related pathologies such as cerebral microbleeds (CMBs). Manual identification of CMBs is time-consuming, has limited reliability and reproducibility, and is prone to misinterpretation. In this paper, a novel computer-aided microbleed detection technique based on machine learning is presented: First, spherical-like objects (potential CMB candidates) with their corresponding bounding boxes were detected using a novel multi-scale Laplacian of Gaussian technique. A set of robust 3-dimensional Radon- and Hessian-based shape descriptors within each bounding box were then extracted to train a cascade of binary random forests (RF). The cascade consists of consecutive independent RF classifiers with low to high posterior probability constraints to handle imbalanced training sets (CMBs and non-CMBs), and to progressively improve detection rates. The proposed method was validated on 66 subjects whose CMBs were manually stratified into “possible” and “definite” by two medical experts. The proposed technique achieved a sensitivity of 87% and an average false detection rate of 27.1 CMBs per subject on the “possible and definite” set. A sensitivity of 93% and false detection rate of 10 CMBs per subject was also achieved on the “definite” set. The proposed automated approach outperforms state of the art methods, and promises to enhance manual expert screening. Benefits include improved reliability, minimization of intra-rater variability and a reduction in assessment time. | Computer-aided detection of cerebral microbleeds in susceptibility-weighted imaging |
S0895611115001445 | Automatic optic disc (OD) detection is an essential step for screening of eye diseases. An OD localization method is proposed in this paper, which aims to locate OD robustly in retinal image with pathological changes. There are mainly three steps in this approach: region-of-interest (ROI) detection, candidate pixel detection, and confidence score calculation. The features of vessel direction, intensity, OD edges, and size of bright regions were extracted and employed in the proposed OD locating approach. Compared with the OD locating method based on vessel direction only, the proposed method could handle the following cases better: OD partially appears in retinal image, retinal vessels are not obvious in retinal image, or there are bright lesions in retinal images. Four public databases with total 340 retinal images were tested to evaluate the performance of our method. The proposed method can achieve an accuracy of 100%, 95.8%, 99.2%, 97.8% for DRIVE database, STARE database, DIARETDB0 database, DIARETDB1 database respectively. Comparison studies showed that the proposed approach is especially robust in the retinal images with diseases. | An approach to locate optic disc in retinal images with pathological changes |
S0895611115001457 | Several transrectal ultrasound (TRUS)-based techniques aiming at accurate localization of prostate cancer are emerging to improve diagnostics or to assist with focal therapy. However, precise validation prior to introduction into clinical practice is required. Histopathology after radical prostatectomy provides an excellent ground truth, but needs accurate registration with imaging. In this work, a 3D, surface-based, elastic registration method was developed to fuse TRUS images with histopathologic results. To maximize the applicability in clinical practice, no auxiliary sensors or dedicated hardware were used for the registration. The mean registration errors, measured in vitro and in vivo, were 1.5±0.2 and 2.1±0.5mm, respectively. | 3D surface-based registration of ultrasound and histology in prostate cancer imaging |
S0895611115001755 | The objective of this study was to develop a quantitative image feature model to predict non-small cell lung cancer (NSCLC) volume shrinkage from pre-treatment CT images. 64 stage II-IIIB NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. For each patient, the planning gross tumor volume (GTV) was deformed onto the week 6 treatment image, and tumor shrinkage was quantified as the deformed GTV volume divided by the planning GTV volume. Geometric, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix image features were extracted from each planning GTV. Prediction models were generated using principal component regression with simulated annealing subset selection. Performance was quantified using the mean squared error (MSE) between the predicted and observed tumor shrinkages. Permutation tests were used to validate the results. The optimal prediction model gave a strong correlation between the observed and predicted tumor shrinkages with r =0.81 and MSE=8.60×10−3. Compared to predictions based on the mean population shrinkage this resulted in a 2.92 fold reduction in MSE. In conclusion, this study indicated that quantitative image features extracted from existing pre-treatment CT images can successfully predict tumor shrinkage and provide additional information for clinical decisions regarding patient risk stratification, treatment, and prognosis. | NSCLC tumor shrinkage prediction using quantitative image features |
S0895611115001767 | Purpose To assess the uncertainty of quantitative imaging features extracted from contrast-enhanced computed tomography (CT) scans of lung cancer patients in terms of the dependency on the time after contrast injection and the feature reproducibility between scans. Methods Eight patients underwent contrast-enhanced CT scans of lung tumors on two sessions 2–7 days apart. Each session included 6 CT scans of the same anatomy taken every 15s, starting 50s after contrast injection. Image features based on intensity histogram, co-occurrence matrix, neighborhood gray-tone difference matrix, run-length matrix, and geometric shape were extracted from the tumor for each scan. Spearman's correlation was used to examine the dependency of features on the time after contrast injection, with values over 0.50 considered time-dependent. Concordance correlation coefficients were calculated to examine the reproducibility of each feature between times of scans after contrast injection and between scanning sessions, with values greater than 0.90 considered reproducible. Results The features were found to have little dependency on the time between the contrast injection and the CT scan. Most features were reproducible between times of scans after contrast injection and between scanning sessions. Some features were more reproducible when they were extracted from a CT scan performed at a longer time after contrast injection. Conclusion The quantitative imaging features tested here are mostly reproducible and show little dependency on the time after contrast injection. | Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors |
S0895611115001779 | Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities. An efficient minimization procedure, attempting to find the global minimum, is applied to the energy functional. The algorithm is evaluated qualitatively and quantitatively using a synthetic example and real MR images of different organs. Comparisons with several state-of-the-art methods demonstrate the superior performance of the proposed technique. Desirable results are obtained even for images with strong and complicated inhomogeneity fields and sparse tissue structures. | An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images |
S0895611115001895 | We present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer–Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: (i) the respiration against anatomical data, and (ii) the X-ray against the Beer–Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools. | Development and validation of real-time simulation of X-ray imaging with respiratory motion |
S0895611115001901 | Positron emission tomography (PET) is a nuclear imaging modality that provides in vivo quantitative measurements of the spatial and temporal distribution of compounds labeled with a positron emitting radionuclide. In the last decades, a tremendous effort has been put into the field of mathematical tomographic image reconstruction algorithms that transform the data registered by a PET camera into an image that represents slices through the scanned object. Iterative image reconstruction methods often provide higher quality images than conventional direct analytical methods. Aside from taking into account the statistical nature of the data, the key advantage of iterative reconstruction techniques is their ability to incorporate detailed models of the data acquisition process. This is mainly realized through the use of the so-called system matrix, that defines the mapping from the object space to the measurement space. The quality of the reconstructed images relies to a great extent on the accuracy with which the system matrix is estimated. Unfortunately, an accurate system matrix is often associated with high reconstruction times and huge storage requirements. Many attempts have been made to achieve realistic models without incurring excessive computational costs. As a result, a wide range of alternatives to the calculation of the system matrix exists. In this article we present a review of the different approaches used to address the problem of how to model, calculate and store the system matrix. | System models for PET statistical iterative reconstruction: A review |
S0895611115001913 | Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast. | A coronary artery segmentation method based on multiscale analysis and region growing |
S0895611115001925 | In this paper, a level set model without the need of generating initial contour and setting controlling parameters manually is proposed for medical image segmentation. The contribution of this paper is mainly manifested in three points. First, we propose a novel adaptive mean shift clustering method based on global image information to guide the evolution of level set. By simple threshold processing, the results of mean shift clustering can automatically and speedily generate an initial contour of level set evolution. Second, we devise several new functions to estimate the controlling parameters of the level set evolution based on the clustering results and image characteristics. Third, the reaction diffusion method is adopted to supersede the distance regularization term of RSF-level set model, which can improve the accuracy and speed of segmentation effectively with less manual intervention. Experimental results demonstrate the performance and efficiency of the proposed model for medical image segmentation. | A novel level set model with automated initialization and controlling parameters for medical image segmentation |
S0895611115001937 | A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention. | A multi-center milestone study of clinical vertebral CT segmentation |
S0895611116300015 | The automatic annotation of medical images is a prerequisite for building comprehensive semantic archives that can be used to enhance evidence-based diagnosis, physician education, and biomedical research. Annotation also has important applications in the automatic generation of structured radiology reports. Much of the prior research work has focused on annotating images with properties such as the modality of the image, or the biological system or body region being imaged. However, many challenges remain for the annotation of high-level semantic content in medical images (e.g., presence of calcification, vessel obstruction, etc.) due to the difficulty in discovering relationships and associations between low-level image features and high-level semantic concepts. This difficulty is further compounded by the lack of labelled training data. In this paper, we present a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR). CBIR is a well-established image search technology that uses quantifiable low-level image features to represent the high-level semantic content depicted in those images. Our method extends CBIR techniques to identify or retrieve a collection of labelled images that have similar low-level features and then uses this collection to determine the best high-level semantic annotations. We demonstrate our annotation method using retrieval via weighted nearest-neighbour retrieval and multi-class classification to show that our approach is viable regardless of the underlying retrieval strategy. We experimentally compared our method with several well-established baseline techniques (classification and regression) and showed that our method achieved the highest accuracy in the annotation of liver computed tomography (CT) images. | Adapting content-based image retrieval techniques for the semantic annotation of medical images |
S0895611116300027 | Cell segmentation is an important element of automatic cell analysis. This paper proposes a method to extract the cell nuclei and the cell boundaries of touching cells in low contrast images. First, the contrast of the low contrast cell images is improved by a combination of multiscale top hat filter and h-maxima. Then, a curvelet initialized level set method has been proposed to detect the cell nuclei and the boundaries. The image enhancement results have been verified using PSNR (Peak Signal to noise ratio) and the segmentation results have been verified using accuracy, sensitivity and precision metrics. The results show improved values of the performance metrics with the proposed method. | Curvelet initialized level set cell segmentation for touching cells in low contrast images |
S089561111630009X | Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method. | In vivo MRI based prostate cancer localization with random forests and auto-context model |
S0895611116300167 | Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning architecture called Transformed Deep Convolution Network (TDCN). This new architecture can unsupervisely fuse image features from different modalities and automatically rectify the pose of vertebra. The fusion of MR and CT image features improves the discriminativity of feature representation and enhances the invariance of the vertebra pattern, which allows us to automatically process images from different contrast, resolution, protocols, even with different sizes and orientations. The feature fusion and pose rectification are naturally incorporated in a multi-layer deep learning network. Experiment results show that our method outperforms existing detection methods and provides a fully automatic location+naming+pose recognition for routine clinical practice. | Multi-modal vertebrae recognition using Transformed Deep Convolution Network |
S0895611116300179 | Studying morphological changes of subcortical structures often predicate neurodevelopmental and neurodegenerative diseases, such as Alzheimer's disease and schizophrenia. Hence, methods for quantifying morphological variations in the brain anatomy, including groupwise shape analyses, are becoming increasingly important for studying neurological disorders. In this paper, a novel groupwise shape analysis approach is proposed to detect regional morphological alterations in subcortical structures between two study groups, e.g., healthy and pathological subjects. The proposed scheme extracts smoothed triangulated surface meshes from segmented binary maps, and establishes reliable point-to-point correspondences among the population of surfaces using a spectral matching method. Mean curvature features are incorporated in the matching process, in order to increase the accuracy of the established surface correspondence. The mean shapes are created as the geometric mean of all surfaces in each group, and a distance map between these shapes is used to characterize the morphological changes between the two study groups. The resulting distance map is further analyzed to check for statistically significant differences between two populations. The performance of the proposed framework is evaluated on two separate subcortical structures (hippocampus and putamen). Furthermore, the proposed methodology is validated in a clinical application for detecting abnormal subcortical shape variations in Alzheimer's disease. Experimental results show that the proposed method is comparable to state-of-the-art algorithms, has less computational cost, and is more sensitive to small morphological variations in patients with neuropathologies. | Statistical shape analysis of subcortical structures using spectral matching |
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