In this paper, we present a fully automated system that detects and segments potential liver cancer tumors from a thin slice CT data. The system is targeted toward a tumor whose volume is larger than $1 cm^3$, 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 computation and memory requirements. It then detects candidate locations as local minima of the intensity fields after a variant of textit{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. The development of the system is a part of the 2008 3D Segmentation in the Clinic: A Grand Challenge competition. Four CT volumes containing 10 tumors were used for the development of the algorithm. Additional six CT volumes containing 10 tumors were used to test the segmentation performance.