Texture analysis of liver cirrhosis

Abstract

Liver with cirrhosis emerges when the cells of liver begin to die and the tissues become a functional knot from these. In the diagnosis of fibrosis, the needle biopsy is a golden standard. Although this technique is a good techique in reaching accurate diagnosis, its being an invasive method arises disadvantage. The developments in medical image processing and artificial intelligence techniques have advanced the potential of using diagnosis system in classification of liver tissues. In this study, we have aimed at producing some objective measures using image analysis, which will be of assistance in the diagnosis of cirrhosis. In order to differentiate between regions of liver with cirrhosis and healthy parenchymal tissues, we have used first order statistical texture features and second order texture features computed from gray level cooccurrence matrix of liver computerized tomography (CT) images. Then liver CT images of healthy people and people with cirrhosis have been classified with support vector machines (SVM) by using all these acquired features. The most successful classification has been calculated as 85.19% with the method of 10 fold cross-validation.