A Novel Level Set Model for Image Segmentation with Interactive Label Regularization Term

Abstract

We propose an interactive level set segmentation method with a novel user’s label regularization term. This new edge-based model can force the evolution of level set function to follow the hard constraints given by the user and effectively solve the problem that user can’t extract some specified object in the image due to the local optimum. The new regularization term is constructed by multiplication of the hard constraints and the level set function. Since the regularization term we defined only works on the pixels labeled by the user, the evolution of level set function can be affected accurately by user’s label for foreground and background. Experimental results are provided to demonstrate the efficiency and accuracy of the new model. Our method can make the segmentation accurately reflect the user’s label. The new method supports the real-time feedback in the segmentation process. We also analyzed the weight of the regularization term, and the best weight of the regularization term is provided.