Abstract: The classical CV model is not completely suitable to segment the gray image which is intensity inhomogeneity,and has been disturbed by Gaussian noises with some variance.The variational CV model based on the total Bregman divergence was proposed and its iterative segmentation algorithm was presented.Firstly,the problems and disadvantages of the variational CV model segmentation method constructed by the Euclidean metric are analyzed.Secondly,compared with Euclidean metric,a figure shows the advantages of the total Bregman divergence that there is no connection with coordinate system in the distance calculation.Then,to reach the purpose of reducing noise sensitivity and enhance robustness of image segmentation,the data deviation term in CV model is built by the total Bregman divergence.Finally,Euler-Lagrange equation of this proposed variational model is obtained by variational method,and the variational model algorithm of the image segmentation is presented by numerical computation method.In addition,to accelerate the convergence rate,the weighting parameters of fitting terms should appropriately chose bigger value,and the importance of fitting items increases in variational model.The experimental results show that the proposed method is low sensitive to initialize contour curve,and has good anti-noise and robust performance.

Key words: Image segmentation,CV model,Level set method,Total Bregman divergence