A hybrid region-boundary model for cerebral cortical segmentation in MRI.
Automatic segmentation of cerebral cortex in magnetic resonance imaging (MRI) is a challenging problem in understanding brain anatomy and functions. The difficulty is mainly due to variable brain structures, various MRI artifacts and restrictive body scanning methods. This paper describes a hybrid model-based method for obtaining an accurate and topologically-preserving segmentation of the brain cortex. The approach is based on defining region and boundary information using, respectively, level set and Bayesian techniques, and fusing these two types of information to achieve cerebral cortex segmentation. It is automatic and robust to noise, intensity inhomogeneities, and partial volume effect. Another particularity of the proposed approach is that bias field is corrected during segmentation process and that the central layer of the cortex is accurately obtained through a topology correction step. The proposed method is evaluated on both simulated and real data, and compared with existing segmentation techniques. The obtained results have demonstrated its improved performance and robustness.