SVM Approach to Classifying Lesions in USG Images with the Use of the Gabor Decomposition

  • Marcin Ciecholewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)


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.


Support Vector Machine Kernel Function Active Contour Model Support Vector Machine Algorithm Gaussian Kernel Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcin Ciecholewski
    • 1
  1. 1.Institute of Computer ScienceJagiellonian UniversityKrakówPoland

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