Parameter Estimation and Two-Stage Segmentation Algorithm for the Chan-Vese Model

The Chan-Vese model is very efficient in segmenting images. However, the algorithm given by Chan and Vese is sensitive to the initial level set function and the regularization parameter. It is difficult to get a right segmentation if the initial level set function and the regularization parameter are not chosen properly. In this paper, we aim to automatically and accurately segment binary images . We propose a two-stage segmentation algorithm and an adaptive parameter estimation method for the regularization parameter. Experiments on some synthetic images and real images show that the proposed algorithm is very efficient.