Characterization of Liver Tissues in CT Scan Images using Texture Analysis

Abstract

This study is an attempt to study the liver CT Scan images using Texture Analysis Techniques and hence the main objective of this study was to characterize the liver tissues in an CT Scan images into three classes which includes; fatty, cirrhosis and normal tissue types by using texture analysis. The texture were extracted from spatial gray level dependence matrix using a window of 20×20 pixels of angle zero and distance equal one pixel. The images were collected from 60 patients represents the classes of the study in the period from6/2011 to 10/2012. Then the textural features were extracted from selected sub-images that showed only the class of interest. The classification technique were adopted as a method of pattern identification the images into three classes. A linear discriminant analysis using stepwise were used to classify the sample into the predefined classes. The stepwise selected 9 features out of fifteen features as the most discriminant features; they included: Entropy, energy, inertia, inverse difference moment, difference entropy, sum variance, difference variance and variance of (SGLD)matrix. The result of this study showed that the total classification accuracy was 93.3%, with an accuracy of 85.1% 98.4% and 94.9% for fatty, cirrhosis and normal respectively