Normal Vector Projection Method used for Convex Optimization of Chan-Vese Model for Image Segmentation

2016 International Conference on Communication, Image and Signal Processing

物理学;无线电电子学;计算机科学

Wei, W.B.^1 ; Tan, L.^1 ; Jia, M.Q.^1 ; Pan, Z.K.^1

College of Computer Science and Technology, Qingdao University, Qingdao

266071, China^1

关键词: Binary level sets;    Convex relaxation;    Evolutionary process;    Global optimal solutions;    Projection formulas;    Signed distance function;    Threshold methods;    Variational level set methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/787/1/012016/pdf
DOI  :  10.1088/1742-6596/787/1/012016

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The variational level set method is one of the main methods of image segmentation. Due to signed distance functions as level sets have to keep the nature of the functions through numerical remedy or additional technology in an evolutionary process, it is not very efficient. In this paper, a normal vector projection method for image segmentation using Chan-Vese model is proposed. An equivalent formulation of Chan-Vese model is used by taking advantage of property of binary level set functions and combining with the concept of convex relaxation. Threshold method and projection formula are applied in the implementation. It can avoid the above problems and obtain a global optimal solution. Experimental results on both synthetic and real images validate the effects of the proposed normal vector projection method, and show advantages over traditional algorithms in terms of computational efficiency.