Non-Invasive Technique to Classify Cirrhotic Liver Using Texture Parameters

Abstract

Texture analysis is an outstanding and fundamental task being used in many medical and computer vision applications. Malfunctioning of the human liver upsets almost all the other organs in the human body. Usually liver infections are difficult to analyze because of inconclusive side effects. More often, the liver could be confronting critically but it may not be significantly unveiled. The main objective of this research work is to provide some standard liver diagnostic measures to minimize the risk factors, as better diagnosis is essential requirement in radiology. The Computerized Tomography (CT) contributes important information to the clinical evaluation of diffuse liver diseases. Haralick texture parameters have been computed on the selected Regions of Interest. B11 is used for discrimination and interpretation of normal and cirrhotic liver diseases. Normal and diseased Liver CT images were collected from Bahawal Victoria Hospital. Normal and cirrhotic liver samples of clinically verified patients were obtained and total 900 Regions of Interest (ROIs) were taken from the selected data. Training of the classes was next step after texture parameter computation. In this work, supervised classification method was used to classify the selected images. In this way, the classes were trained in a supervised manner. The maximum accuracy obtained during this research work was 100%, linear dimensionality was 1 and the linear separability was 0.99%. Results of this research work suggested that texture parameters have high degree of reliability to automatically discriminate similar tissue textures, when regions are obvious. This framework separates benign from malignant liver tumors with moderately high precision and is therefore link up the psychophysics with machine vision to outline, recognize, categorize or discriminate textures.