A Novel Hybrid Segmentation Method for Medical Images Based on Level Set

In this paper, integrating boundary and region information of medical images, we propose a novel hybrid segmentation method based on level set. The main contributions of this paper are to modify the velocity function for the boundary-based level set method, and to design a novel energy function as a stopping criterion. This velocity function is modified according to the statistical characteristics of the segmented regions during the evolution so that the medical images with weak boundary and concave region can be segmented. The stopping criterion depends on not only the boundary information of the image but also the statistical characteristics of the segmented regions, which can overcome the over-segmentation effectively. Furthermore, our method forces the level set function close to a signed distance function, therefore, eliminates the complex re-initialization procedure and reduces the side effects of re-initialization. Experimental results for real clinical images show the effectiveness of our method.