标题：Two algorithms for level set method preserving signed distance functions
作者：Liu, Cun-Liang ;Pan, Zhen-Kuan ;Zheng, Yong-Guo ;Duan, Jin-Ming ;Zhang, Feng
作者机构：[Liu, Cun-Liang ;Pan, Zhen-Kuan ;Duan, Jin-Ming ;Zhang, Feng ] College of Information Engineering, Qingdao University, Qingdao 266071, China;[Liu, Cun 更多
来源：Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
摘要：The well-known Chan-Vese model has been widely used in image segmentation. However, the original model is limited by two important numerical issues. Firstly, the level set method does not implicitly preserve the level set function as a signed distance function. Secondly, the level set method is slow because of the gradient descent equation. In this paper, two fast algorithms, a dual method and a split Bregman method, were proposed to improve the computation efficiency. In order to force the level set function to be a signed distance function during evolution, a projection approach was proposed to solve the constraint, and then the augmented Lagrangian method was used to speed up the convergence rate. The experimental results demonstrate that the proposed methods not only have better performance, but also are more efficient than the gradient descent method.