An Improved C-V Model without Reinitialization
Yunping Zhang, Yan Hao Huang, Meiqing Wang · 2009
In this paper the Chan-Vese model is analyzed. An improved Chan-Vese model without reinitialization is proposed to overcome the drawbacks of the Chan-Vese model. The internal energy proposed by Li model, the energy items based on image gradient are used to improve the Chan-Vese model; and the Euclidean norm of the gradient of the level set function is used to replace the regularized Dirac function in the Chan-Vese model for keeping segmentation stability and eliminating the restraining of Dirac function. The experimental results show that the segmentation results by the proposed method in this paper are better than the Chan-Vese model and the Li model when processing images with "hole" and "thick" edges, multi-target images or real images with noise, complex details and borders.