Author(s): Meenu Sharma, Rafat Parveen

Liver cancer is becoming a major reason of death globally in most of the cases diagnoses of the liver cancer are take place only at its late stage, it becomes late to cure, that leads to increase the number of people who dies due to liver cancer. If it is diagnosed in its early stage then the possibility will increase for curing the suffered peoples. It becomes necessary to trea t liver cancer in its early stage. Digital image processing technique is a non-invasive widely used way which play most important role in early detection and prediction of liver cancer and applied when the input data is in the form of images. For detecting liver tumors or nodules numerous tests of patients are required, automated diagnosis system utilizing digital image techniques employed for the prediction and detection of Liver cancer at early stage, plays a vital role in decreasing the mortality rate because of early detection of Liver cancer. In this paper, a comparative table has been presented by analyzing various existing image processing techniques used for early detection and prediction of Liver cancer with their accuracy. This comparative table, shows that for CT scan images higher accuracy is obtained by k means segmentation, active contour segmentation and GLCM with SVM classifier.