Multiresolution convex variational model for multiphase image segmentation

This paper presents a convex variational model for multiphase image segmentation by incorporating a multiresolution approach. We extend our previous work to formulate the energy functional which is robust response to image variations. In contrast to our previous work, which can lead to local minima, a global solution is proposed to minimize the segmentation energy with some constraint conditions. By incorporating edge-based information, a non-convex energy functional is first introduced on the membership functions, which are used as indicators of different homogeneous regions. Then the non-convex problem is converted into a continuous convex formulation. An efficient dual minimization implementation of our binary partitioning function model accurately describes disjoint regions using stable segmentation. Experiments results show the proposed model is robust to noise, independent of initialization and unambiguous segmentation. Compared with the traditional variational models, the proposed model can get more accurate results and higher computational efficiency.