Variational nonlocal image segmentation using split-Bregman

Authors:
Ning He;Ke Lu;Qian Wang
Affiliations:
Union University, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Year:
2012

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Abstract

In this paper, we proposed an image segmentation model in a variational nonlocal means framework. The model has following advantages. Firstly, Theconvexity global minimize optimum informations are taken into account and got the better segmentation results; secondly, the proposedglobal convex energy functional combined the nonlocal regularization and the local intensity fitting terms. The nonlocal total variational (TV) regularization term can preserve the detail structures of the target objects. At the same time, the modified locally binary fitting (LBF) term introduced to the model as the local fitting term can efficiently deal with the intensity inhomogeneity images; finally, we apply the split Bregman method to minimize the proposed energy functional efficiently. Weapplied the proposed model to the real medical images and extent to sensing images. Comparing with other models, the proposed model not only demonstrates accuracy but also display superiority.