passages.id: PMC-3158436-sec-06__text
passages.type: sec
passages.text: 2.1.3
  Pixel Classification
  The feature vector of a grid, which consists of nine features, is normalized to [0, 1], and the position features are scaled by the weighting coefficient wpos. Then, taking the nine dimensional vector of each grid as the input of the classifier, the ROI and non-ROI background in ultrasonic breast tumor images can be distinguished.
  In this section, the self-organizing feature map neural network (SOMNN) [25], proposed by Kohonen according to characteristics of the nervous system, is used for the classification of these grids to detect the ROI automatically. The SOM is trained by the unsupervised learning to produce a low-dimensional discretized representation of the input space of the training samples, and it uses a neighborhood function to preserve the topological properties of the input space, which makes it different from other artificial neural networks.
  In this way, the SOM is able to classify the sub-blocks into n clusters only by the unsupervised learning. In our study, the output layer number n is set to 2 (ROI and non-ROI output) and the feature vector extracted from each grid is taken as the input. To map the sorted sub-blocks back to the size of the original ultrasonic images, the initial result of ROI detection can be represented by a binary image as shown in Fig. (2d), where white areas indicate the ROI candidates while black areas the non-ROIs.
  