Gabor Wavelets and SVM Classifier for Liver Diseases Classiflcation from CT Images

In this paper, a liver diseases diagnosis based on Gabor wavelets and support vector machine (SVM) classifier is proposed. The diagnosis scheme includes two steps: features extraction and classification. The features derived from Gabor wavelets are obtained from the regions of interest (ROIs) among the normal and abnormal CT images. In the classification step, the SVM classifier is used to discriminate the fiver diseases. Finally the receiver operating characteristic (ROC) curve is employed to evaluate the performance of the diagnosis system. Three kinds of fiver diseases are identified including cyst, hepatoma and cavernous hemangioma. The effectiveness of the proposed method is demonstrated through experimental results on CT images including 76 fiver cysts, 30 hepatomas, and 40 cavernous hemangiomas. From the results we can observe that the discrimination rate of cyst is higher than other diseases, and the classification accuracy decreases slightly between cavernous hemangiomas and hepatomas. However, a normal region can be discriminated from all of these diseases at most entirety.