چکیده (انگلیسی):

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.