Abstract
Liver cancer is one of the major death factors in the world. Transplantation and tumor resection are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations. An efficient and effective automatic liver segmentation is required for corresponding quantitative evaluations. Computed Tomography (CT) is highly accurate 14 liver cancer diagnosis. Manual identification of hepatic lesions done by trained physicians is a time-consuming task and can be subjective depending on the skill, expertise and experience of the physician. Computer aided segmentation of CT images would thus be a great step forward to scientific advancement for medical purposes. The sophisticated hybrid system was proposed in this paper which is capable to segment liver from abdominal CT and detect hepatic lesions automatically. The proposed system based on two different datasets and experimental results show that the proposed system robust, fastest and effectively detect the presence of lesions in the liver, count the distinctly identifiable lesions and compute the area of liver affected as tumors lesion, and provided good quality results, which could segment liver and extract lesions from abdominal CT in less than 0.15 s/slice.