Abstract
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. Four 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.
Highlights
Depending on which part of the brain has been affected
Support Vector Machine classifier (SVM) and Adaboost classifiers have been used for classification of symptomatic and asymptomatic images
The adaboost classifier yielded the classification accuracy of 75.1, 79.4 and 81.3% respectively for wavelet packet, curvelet and contourlet transform features. For both SVM and adaboost classifier the classification accuracy is the highest for contourlet transform features compared to accuracy of wavelet packet and curvelet transform
Summary
Depending on which part of the brain has been affected. An ischemic stroke may be caused by different kinds of Stroke has been one of the leading causes of death in the world and about 6.2 million people die each year around the globe due to stroke. Transient Ischemic Attack (TIA) is a set of symptoms that occur as a result of a temporary lack of blood supply to the brain. Atherosclerosis is a condition in which carotid artery becomes narrow due to accumulation of calcium and fatty substances. These excessive building up of these substances form plaques in the lumen area of the artery. Discrete wavelet features along with higher order spectra and texture features have been used for testing the performance of SVM classifier with different kernels (Acharya et al, 2011).
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