A neighbor level set framework minimized with the split Bregman method for medical image segmentation

Medical image segmentation has a huge challenge due to intensity inhomogeneity and the similarity of the background and the object. To meet this challenge, we propose an improved active contour model, in which we combine the level set method and the split Bregman method, and provide the two-phase formulation, the multi-phase formulation and 3D formulation. In this paper, the proposed model is presented in a level set framework by including the neighbor region information for segmenting medical images in which the energy functional contains the data fitting term and the length term. The neighbor region and the local intensity variances in the data fitting term are designed to optimize the minimization process. To minimize the energy functional then we apply the split Bregman method which contributes to get faster convergence. Besides, we extend our model to the multi-phase segmentation model and the 3D segmentation model for cardiac MR images, which have all achieved good results. Experimental results show that the new model not only has strong robustness to other cardiac tissue effects and image intensity inhomogeneity, but it also can much better conduce to the extraction of effective tissues. As we expected, our model has higher segmentation accuracy and efficiency for medical image segmentation.