Parallelizable and robust image segmentation model based on the shape prior information

Image segmentation is an important task in many fields, and there are plentiful models based on region or edges. Nowadays, the speed of calculation and the universal applicability of the model attract much attention. To some extent, the traditional energy model can segment images suffering from intensity inhomogeneity while it relies on initialization seriously. In this paper, we present a new model that consists of an arbitrary active contour model and proposed shape priori information term, which can segment various images accurately and provide an opportunity to carry on parallelizable calculation. The shape priori information term plays a key role in our energy functional and the shape priori information can be chosen diversely. This term also improves the robustness of our model including initial conditions and parameter adjustment. Besides, the split Bregman method is then applied to minimize the energy functional. Multiple experimental results and comparisons are shown to demonstrate the superiority of the proposed model. Firstly, fuzzy clustering, threshold and manual operation are used to be the shape priori information. Secondly, it is illustrated that our model is not sensitive to parameters and initial contours. Computation time and accuracy are also obviously improved when using the parallel algorithm.