Abstract
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.
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Ciecholewski, M. (2011). SVM Approach to Classifying Lesions in USG Images with the Use of the Gabor Decomposition. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_9
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DOI: https://doi.org/10.1007/978-3-642-23878-9_9
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