Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach

Abstract. In this paper, we present a fully automated system that de-tects and segments potential liver cancer tumors from a thin slice CT data. The system is targeted toward a tumor whose volume is larger than 1cm3, and is efficient as the average computation time per volume in our experiment is roughly 3.5 minutes. The system first reduces the volume size by 4x4x4 to reduce the com-putation and memory requirements. It then detects candidate locations as local minima of the intensity fields after a variant of elastic quadratic smoothing. It then provides a rough segmentation at each candidate by fitting a plane at sampled points near the periphery of the concave region in the intensity profiles. The rough segmentation is used to estimate the range of intensity values in the tumor, which is used to obtain a more accurate segmentation by a method originally developed for pulmonary nodules. The result of the second segmentation is interpolated at the resolution of the original data