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Gallbladder boundary segmentation from ultrasound images using active contour model
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Gallbladder segmentation in 2-D ultrasound images using deformable contour methods
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
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IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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The article presents the application of the support vector machines (SVM) method to recognise gallbladder lesions such as lithiasis and polyps in USG images. USG images of the gallbladder were first processed by the histogram normalisation transformation to improve their contrast, and the gallbladder shape was segmented using active contour models. Then the background area of uneven contrast was eliminated from images. To extract features from the images to be classified, the Gabor decomposition was applied to a plane presented in a log-polar system. In the best case, the SVM achieved the accuracy of 82% for all lesions, 85.7% for lithiasis and 74.3% for polyps.