Abstract :Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and Image processing techniques have significantly enhanced
interpretation of medical images. Computer Aided Diagnosis (CAD) systems plays a major role in early detection of liver disease and in reducing liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. The liver lesions are classified as
malignant and benign based on feature difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers
are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and
imaging devices and setting.