A Convex Model for Segmentation of Tissues for Brain MR Images Based on Mutual Information Maximization

In segmentation of tissues for brain MR images,major difficulties arise from intensity inhomogeneities.Unfortunately,satisfactory segmentation results are hard-to-get using conventional segmentation techniques on MR images perturbed by intensity inhomogeneities.This paper presents a convex variational level-set segmentation model based upon the maximization of mutual information for brain MR images.First,an image segmentation model is developed aiming at maximizing the energy of mutual information between image intensity and label,in which the information of bias field is incorporated.The model is then equivalently related as a convex model with a level set method formulation.After the convexification,the model is modified by replacing the TV-norm by a weighted TV-norm given by the edge indicator function.Then the convex energy is easily minimized utilizing a Split Bregman iteration scheme.The proposed approach has capability of carrying out the segmentation of brain tissues and the estimation of bias field simultaneously.Experimental results demonstrate that our model is efficient and robust against the noise and the intensity non-uniformity.