Reducing over- and Undersegmentations of the liver in computed tomographies using anatomical knowledge

In the last decades several liver segmentation methods have been proposed. The proposed methods go from region growing to the more complex statistical shape models. Despite the robustness of those algorithms, liver segmentation is still a challenging task especially in areas in which its neighboring organs have similar intensities, e.g., heart and ribcage. In addition to this, pathological organs that contain tumors near their surface present additional difficulties. This paper presents a solution to increase the accuracy of those algorithms in the aforementioned areas. The effect of the improvement using the generated heart and ribcage walls (7% and 1% respectively) is evaluated on 9 clinical computer tomographies (CT). The improvement (12 %) when tumors are near the surface, on the contrary, is tested on 7 clinical CT images.