A Tree-Like Multiphase Level Set Framework for 2-D and 3-D Medical Image Segmentation

The Chan-Vese model (C-V model) using one level set function is a two-phase active contour model and has limitation for medical image segmentation. In this paper, a treelike multiphase level set framework based on the C-V model is proposed which applies the C-V model for multiple times to obtain multiple phases (n-1 times for n phases, n>1) and each operates as follows: first, a medical image is partitioned into an object and a background by the C-V model; second, the pixels in the background is replaced by the mean of the object to create a new image (Hereafter, we name this step as the technique of painting background); third, the new image is partitioned by the C-V model for the second time to obtain a new object. The proposed framework ensures that the new object is only inside the previous one. Experimental results for two dimensional (2D) and three dimensional (3D) medical images show that the framework is able to detect the objects with weak boundaries