An Optimal Initialization Technique for Improving the Segmentation Performance of Chan-Vese Model

In level set method, initialization mode not only influences evidently the implementation efficiency but also relates directly to the final results. The paper presents an new initialization scheme for improving the segmentation performance of Chan-Vese model. The proposed initialization scheme consists of two stages. The first stage computes rough edges by using canny edge detection operator. The second stage removes noise edges and redundant edges by a morphological filter, and generates closed contours by iteratively connecting edge points according to a local cost function. In comparison with the primal Chan-Vese model, experimental data show that the Chan-Vese model equipped with our initialization scheme provides superior segmentation results and takes less computational cost.