Accurate and automatic brain tissue segmentation of magnetic resonance (MR) images is a challenging problem because of partial volume (PV) effects

intensity non-uniformity (INU

also known as bias field) and noise.We present an efficient and accurate

fully automatic two-dimensional (2D) and three-dimensional (3D) algorithm for segmenting brain MR images into anatomical major tissue classes such as white matter (WM)

gray matter (GM) and cerebrospinal fluid (SCF).Our algorithm is formulated by proposing an objective function based on standard FCM algorithm with bias field correction and neighborhood constrain.In our algorithm

a parameterized model is adopted to express the INU and a neighbor constrain similar to Markov random field (MRF) is proposed to express spatial consistency of brain tissue.Experimental results with both synthetic and real data are included

as well as comparisons of the performance of our algorithm with that of other published methods.The validation of the algorithm shows good accuracy and fast convergence.