Image segmentation is the process of subdividing the image to into its parts that are constituent and is considered one of the most difficult tasks in image processing. It plays a task that is a must any application and its particular success is based on the effective implementation of the segmentation technique. For numerous applications, segmentation reduces to locating an object in an image. This involves partitioning the image into two classes, background or object. Into the individual system that is visual segmentation happens obviously. Our company is experts on detecting patterns, lines, edges and forms, and making decisions based upon the information that is visual. At that time that is same we have been overwhelmed by the quantity of image information which can be captured by technology, as it is not feasible to manually process all such images.Automatic segmentation of tumor faction from medical pictures is difficult due to size, shape, place and presence of other objects with the intensity that is exact same in the image. Therefore, cancer segments from the liver where tumor persists cannot be easily segmented accurately from medical scans utilizing approaches that are traditional. The performance of ANN been examined in classifying the Liver Tumor in this research. An approach for segmentation of tumor and liver from medical pictures is principally used for computer aided diagnosis of liver is required. The method is use contour detection with optimized threshold algorithm. The liver is segmented region that is utilizing technique efficiently close around the liver tumors. The whole process is a learning that is supervised; the classifiers require training information set which can be segmented. The classifier that is last evaluated with test set total error in tumor segmentation of this liver is be calculated. Algorithm should be based on segmentation of abnormal regions in the liver. The category regarding the regions can be carried out based on shape categorization and lots of other features using methods such artificial networks that are neural.