An improved Chan-Vese model without reinitialization for medical image segmentation

In this paper, an improved variational level set method for the Chan-Vese model is proposed to drive level set function to become fast and stably close to signed distance function. A restriction item that is a nonlinear heat equation with balanced diffusion rate is added to the traditional Chan-Vese model, and therefore the costly re-initialization procedure is completely eliminated. The proposed variational level set formulation is implemented by numerical scheme with spatial rotation-invariance gradient and divergence operator. Consequently it computes more efficiently. The proposed algorithm has been applied to medical images with desired results.