Prediction of Cirrhosis Based on Singular Value Decomposition of Gray Level Co-occurence Marix and aNneural Network Classifier

In this present work, a technique for discrimination between normal and cirrhotic liver segmented regions of interest (SROIs) based on singular value decomposition (SVD) of GLCM matrix is reported. Thirty four B-mode ultrasound images taken from 22 normal volunteers and 12 patients suffering from liver cirrhosis were collected from Department of Radio diagnosis and Imaging, PGIMER, Chandigarh, India. Firstly, the gray level co-occurrence matrix (GLCM) texture features are computed for 121 SROIs (82 normal SROIs, 39 cirrhotic SROIs) and classification is done using a neural network (NN) classifier. The classification accuracy of 95.86% is achieved without feature selection. Secondly, feature selection is carried out by two different approaches. In approach 1, standard correlation based feature selection (CFS) is used to find the optimal subset of GLCM texture features which provides best discrimination between normal and cirrhotic SROIs. It has been observed that CFS method,results in an optimal subset of 7 GLCM texture features {angular second moment (ASM), Contrast, Variance, Sum Average, Entropy, Difference Entropy and Information Measures of Correlation-1}. In approach 2, the potential of singular values obtained by singular value decomposition (SVD) of GLCMs for discrimination between normal and cirrhotic SROIs is investigated. It has been observed that only first 2 singular values can provide effective discrimination between normal and cirrhotic liver SROIs. In the classification stage a neural network (NN) classifier is used. The classification accuracy of 95.04% is obtained in both cases. From the comparison it is concluded that only first two singular values obtained by SVD decomposition of the GLCMs and a NN classifier can be used to build acomputationally efficient computer aided diagnostic (CAD) system for predicting liver cirrhosis.