Carotid plaque Classification using Contourlet Features and Support Vector Machines

The aim of this study is to propose a suitable and reliable system for better diagnosis and treatment of carotid diseases. In this study, Computer Aided Diagnosis (CAD) system has been proposed for classifying carotid artery plaques using Contourlet features. Carotid images have been acquired for 124 subjects with symptoms (Amaurosis Fugax, Stroke or Transient Ischemic Attack) and 133 subjects with no symptoms in the recent past. Images were normalized and plaque regions have been manually segmented by experts and these Region Of Interests (ROI) have been used for further processing. 4 level Contourlet transform has been applied to all ROIs and subimages were produced at different scales and orientations. Energy, Entropy, Mean and Standard deviation features were extracted from all the subimages. The feature selection has been done to select significant features and to ignore insignificant ones. Support Vector Machine classifier (SVM) and Adaboost classifier have been applied to the selected features and plaques were classified as symptomatic or asymptomatic plaques. The contourlet features with Support vector machine classifier produced classification accuracy of 85.6% compared to 81.3% accuracy in Adaboost classifier. The classification results were compared with curvelet transform features and wavelet packet features. The contourlet with SVM classifier yielded better performance compared to curvelet and wavelet packet.