A convex neighbor-constrained active contour model for image segmentation

A large number of real images possess the property of intensity non-homogeneity, which hinders them from being segmented by many image segmentation approaches. Recently, region-based active contour models utilizing local information have been introduced to segment images with intensity non-homogeneity. However, all these models are not convex, thus a good initial guess is required, which limits their practical application. In this paper, we propose a convex neighbor-constrained active contour model to segment images with intensity non-homogeneity. With different shapes and sizes of the neighborhood for each point, our model can accurately capture the region information of a given image. Our model is convex, and therefore it is independent of the initial condition and allows for automatic segmentation. To minimize energy functional of the model, we choose the efficient and fast Split Bregman method. Experimental results on synthetic and real images demonstrate the superior performance of our model.