Improving the Classification of Cirrhotic Liver by using Texture Features.

We have been developing a computer-aided diagnosis (CAD) system for distinguishing the cirrhosis in MR images by shape and texture analysis. Two shape features are calculated from a segmented liver region, and seven texture features are quantified by using grey level difference method (GLDM) within the small region-of-interests (ROIs). The degree of cirrhosis is derived from integrating the shape and texture features of the liver into a three-layer feed-forward artificial neural network (ANN). A liver is regarded as cirrhosis if the percentage of the ROIs with a degree over 0.5 is greater than 50%. The initial experimental result showed that the ANN can learn all of the patterns in the training data sets. In testing of the whole liver regions, 82% cirrhosis and 100% normal cases were correctly differentiated from 18 test cases, that indicates our proposed method is effective to the cirrhosis prediction on MRI.