COMPUTER AIDED DIAGNOSTIC SYSTEM FOR HEPATIC LESIONS

Abstract

Liver diseases are among the leading causes of death worldwide. The most useful approach for controlling the growth of disease to reach at severe condition is to treat these diseases at the early stages. Early treatment requires early diagnosis, which needs an accurate and reliable diagnostic procedure. The aim of this study is to develop a computer-aided diagnostic (CAD) system to achieve aforementioned objective. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver lesions such as hepatocellular carcinoma. Although in recent years the quality of CT images has been significantly improved, however in some cases image interpretation by human beings is often limited. So we tried to develop an automated system to detect and classify liver anomalies using CT images. Region of interest from CT images was segmented using Active contours (snakes) algorithm [2] and segmented image is used to extract statistical features by co occurrence matrix [4]. To facilitate the classification of hepatic lesions, Support Vector Machine [10] and neural networks are used. The results show that it is possible to automatically identify patients with liver lesions like Hemengioma, Hepatoma or Cirhhosis based on texture features and that the machine performance is marvelous and can assist human experts