Due to the intensity inhomogeneity and noise in brain MR images

it is difficult for the traditional models to obtain desirable segmentation results. In this paper

we first propose a local energy function based on the fuzzy C-means model (FCM)

which combines segmentation with bias correction. As a result

the proposed model can handle intensity inhomogeneity. Then

the non-local method is used as a regularization term to reduce the impact of noise and to keep the image structure. Finally

the local entropy information is incorporated into the model

which makes it more robust to noise and intensity inhomogeneity. Experiments of the brain magnetic resonance images show that the proposed method can obtain better segmentation results and bias corrected results.