The Lee–Seo model with regularization term for bimodal image segmentation

In this paper, we improve Lee–Seo’ bimodal image segmentation model using a regularization term. This regularization term will maintain the smoothness of the level set function and decrease the level set function’ oscillations around the desired steady state when the noise level is lager. Furthermore, we also provide a rigorous study of the modified model. Based on techniques in calculus of variations, the existence of solutions of the modified model in BV space is established. Based on the theory we present (see ), we constructed a fast convergent algorithm to process images. It turns out our method is twice fast in processing an image than Lee–Seo’s algorithm with the same constant value initial level set function.