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Objective Accurate segmentation of brain tissue in MRI is an essential step and

remains a challenging problem because of acquisition noise，non-uniformities in the MR

magnetic field，partial volume effects and the complex anatomy structure of the brain.

Methods In this paper，we presented a novel approach based on Random Walks (RW) to extract

white matter (WM)，gray matter (GM) and cerebrospinal fluid (CSF). To overcome the

shortcomings of Random Walks，we introduced the concept of local binary patterns (LBP) into

Random Walks to construct a new weighting function. The new weighting function not only

reflected the changing information of adjacent-pixel’s gray value，but also contained the

texture information of local image，which could strengthen the ability of RW to identify

homogeneous pixels and edges. We also achieved a better performance with prior probability

model. Results Experiment results were analyzed against different levels of noise and bias

field，and the proposed method performed better discriminative power of identifying the

brain tissue boundary. Conclusions This improved Random Walks based on LBP segments brain

tissue images accurately.