Robust level set method for medical image segmentation

Abstract

Level set methods provide powerful numerical techniques for analyzing and solving interface evolution problems based on partial differential equations. Level sets display interesting elastic behaviors and can handle topological changes. Although level set methods have many advantages, they still often face difficult challenges such as poor image contrast, noise, and missing or diffuse boundaries. The robust level set method of this paper is based on the anisotropic diffusion method. The fast marching method provides a fast implementation for level set methods, the anisotropic diffusion is allowed to better control the amount of smoothing effect and this process can get both noise smoothing and edge enhancement at the same time. Experimental results indicate that the method can greatly reduce the noise without distorting the image and made the level set methods more robust and accurate.