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from scipy.ndimage import label, find_objects
import numpy as np
import cv2
IMAGE_SPACING_X = 0.7031
IMAGE_SPACING_Y = 0.7031
IMAGE_SPACING_Z = 2.5
def compute_largest_diameter(binary_mask):
# Label connected components in the binary mask
labeled_array, num_features = label(binary_mask)
# Find the objects (tumors) in the labeled array
tumor_objects = find_objects(labeled_array)
# Initialize the largest diameter variable
largest_diameter = 0
# Iterate through each tumor object
for obj in tumor_objects:
# Calculate the dimensions of the tumor object
z_dim = obj[2].stop - obj[2].start
y_dim = obj[1].stop - obj[1].start
x_dim = obj[0].stop - obj[0].start
# Calculate the diameter using the longest dimension
diameter = max(z_dim * IMAGE_SPACING_Z, y_dim * IMAGE_SPACING_Y, x_dim * IMAGE_SPACING_X)
# Update the largest diameter if necessary
if diameter > largest_diameter:
largest_diameter = diameter
return largest_diameter / 10 # IN CM
def generate_features(img, liver, tumor):
contours, _ = cv2.findContours(mask_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
features = {
"lesion size (cm)": compute_largest_diameter(tumor),
"lesion shape": "irregular",
"lesion density (HU)": np.mean(img[tumor==1]),
"involvement of adjacent organs:": "Yes" if np.sum(np.multiply(liver==0, tumor)) > 0 else "No"
}
return features
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