Improved coupled model for MR images segmentation and bias restoration
改进的核磁共振图像分割与偏移场恢复耦合模型

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.