The paper presents the automated computation of hepatic tumor burden from abdominal computed tomography (CT) images of diseased populations with images with inconsistent enhancement. The automated segmentation of livers is addressed first. Here we segment the tumor and classify about the images. Our technique improved significantly the segmentation of large tumors and segmentation. Adaptive threshold method is used for segment the liver. After segmentation we extract feature for the liver. For extracting feature here we use the GLCM (Gray level co-occurrence matrix). This algorithm is used for extracting Statistical information about the images. Such as correlation, entropy, etc… After extracting the feature SVM (support vector machine) classifier is used to classify the images. This classifier will classify the feature and predict the decision about the liver images. For liver tumor segmentation, here we will use fuzzy c means segmentation. Finally the segmented result will be displayed. Liver segmentation errors are reduced significantly and all tumors are detected. Finally, support vector machines and feature selection are employed to reduce the number of false tumor detections.