Adaptive level-set evolution without initial contours for image segmentation

Abstract

We propose a novel variational formulation (external energy) for implicit active contours that forces the level-set function to have the opposite sign along the edges at convergence. This external energy is then incorporated into a variational level-set formulation with two extra regularization terms (internal energy). The resulting evolution of the level-set function is the gradient flow that minimizes the overall energy functional. Because of the external energy, the level-set function can be initialized to any bounded function (e.g., a constant function), which completely eliminates the need of initial contours. This implies that the new formulation is robust to initialization or even free of manual initialization. The proposed model is been applied to both real and synthetic images with promising results. Besides, our model is especially appropriate for segmentation of real images with a complex and /or nonuniform background.