Indirect diffusion based level set evolution for image segmentation

Abstract

In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart.