To assist radiologists and physicians in diagnosing, and in treatment planning and evaluating in liver oncology, we have developed a fast and accurate segmentation of the liver and its lesions within CT-scan exams . The first step of our method is to reduce spatial resolution of CT images . This will have two effects: obtain near isotropic 3D data space and drastically decrease computational time for further processing . On a second step a 3D non-linear "edge -preserving" smoothing filtering is performed throughout the entire exam. On a third step the 3D regions coming out from the second step are homogeneous enough to allow a quite simple segmentation process, based on morphological operations, under supervisor control, ending up with accurate 3D regions of interest (ROI) of the liver and all the hepatic tumors . On a fourth step the ROIs are eventually set back into the original images, features like volume and location are immediately computed and displayed. The segmentation we get is as precise as a manual one but is much faster.