Topology- and convexity-preserving image segmentation based on image registration

Image segmentation aims to extract the target objects or to identify the corresponding boundaries. For corrupted images due to occlusions, obscurities or noises, to get an accurate segmentation result is very difficult. To overcome this issue, the prior information is often introduced and particularly, the convex prior attracts more and more attentions recently. In this paper, we propose a topology- and convexity-preserving registration-based segmentation model, which can be suitable for both 2D and 3D cases. By incorporating the level set representation and imposing the constraints on the suitable regions, we can explicitly force the fully convex segmentation results or the partially convex segmentation results. To solve the proposed model, we employ the alternating direction method of multipliers and numerical experiments on 2/3D synthetic and real images demonstrate that the proposed model can indeed lead to the accurately topology- and convexity-preserving segmentation.