Medical image analysis is helpful for doctors to diagnose diseases. However

the images usually have noise and intensity inhomogeneities

which makes it hard to obtain satisfactory results using the traditional image segmentation methods. To solve these problems

we propose a coupled model based on local image information

which can segment images while restoring the bias field. In order to obtain global optimal results accurately and quickly

we improved the coupled model to be a convex function and solved it based on the Split-Bregman method. The experimental results show that our method can reduce the effect of the noise and intensity inhomogeneities

and obtain more accurate segmentation results while estimating the bias field efficiently.