A novel shape prior based level set method for liver segmentation from MR Images

Abstract

Liver segmentation in MR Image is the foundational work for further research in our lab. Traditional Level Set methods and active contours were often used to segment the liver, but the results were not always promising due to noise and the low gradient response on the liver boundary. In this paper we propose a novel level set based variational approach that incorporates shape prior knowledge into the improved Chan-Vese’s model [1] which can overcome the leakage and over-segmentation problems. Some statistical methods are used to get the prior shape, and the training process allows the prior shape not exactly at the location of desired object. Experiments are taken on abdomen MRI series and the results reveal that our improved level set based shape prior method can segment liver shape precisely and a refined liver perfusion curve without respiration affection can be achieved.