A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the tissue segmentation and quantitative analysis of magnetic resonance images. However

the accuracy of these methods is affected seriously by the intensity inhomogeneities of images. In this paper

We present a novel algorithm(BAFCM) for fuzzy segmentations of images that are subject to intensity inhomogeneities

such as magnetic resonance image. The algorithm is formulated by modifying the objective function in the fuzzy c means algorithm to include a gain field

which models image intensity inhomogeneities. First and second order regulation terms in AFCM algorithm ensure that the gain field is both slowly varying and smooth

but increase complexity of computation greatly. Instead of computing gain field

we compute bias field first

then convert bias field to gain field. With BAFCM

we can correct the intensity inhomogeneities and implement fast classification of human brain tissue of MR image automatically.