Ecient Liver Segmentation exploiting Level-Set Speed Images with 2.5D Shape Propagation

In this paper, we propose an ecient semi-automatic liver segmentation method from contrast-enhanced computed tomography (CT) images. We exploit level-set speed images to define an approximate ini- tial liver shape. The first step divides a CT image into a set of discrete objects based on the gradient information, which is normalized on the speed image. The second step detects the objects belonging to the liver based on 2.5D shape propagation, which models the segmented liver re- gion of the slice immediately above or below the current slice by points being narrow-band of distance from the boundary, and skeletons. In this step, manual inputs of seed points for the topmost slice, the bottommost slice of the liver, and regions that do not connect to the liver from the previous slice are required because there is no prior information for the 2.5D shape propagation. With this optimal estimation of the initial liver shape, our method decreases the computation time by minimizing level- set propagation, which converges at the optimal position within a fixed iteration number. Our method was validated on ten data sets and the results were compared with the manually segmented result. The average score using the comparison metrics for the accuracy evaluation was 75. The average processing time for segmenting one data set was 382s.