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Accurate brain image segmentation is a challenge and meaningful task that assists physicians in the disease diagnosis. In this paper, we present a nonlocal based two-step method for image segmentation. First step is to denoise the MRI brain image with adaptive nonlocal regularization. The second step is our new nonlocal based regularized segmentation. We force the result segmentation of grey matter(GM), white matter(WM) and cerebrospinal fluid(CSF) keeping as much structure as possible by using nonlocal regularization, which has significant meaning in diagnosis. With experiments on synthetic images from BrainWeb and real MRI images from Zhejiang Cancer Hospital, we find that our method performances very well on both databases.

A Nonlocal Based Two-Step Method Applied to MRI Brain Image Segmentation