3D SEGMENTATION OF MRI BRAIN USING LEVEL SET AND UNSUPERVISED CLASSIFICATION

Abstract

The precise volumetric brain segmentation of normal and pathological MR images is a challenging problem in understanding brain anatomy and functions. The requirement to automate this process is another challenge because manual segmentation is extremely laborious. However, it has been proven to be problematic, both due to the high complexity of anatomical structures as well as their large variability, and due to difficulties caused by the presence of noise and artefacts. This paper describes a fully automatic and accurate method for segmenting normal brain tissues and a semi-automatic procedure for delineating meningiomas tissues. The approach performs the segmentation using a succession operations involving a registration step from known data, a classification step and a segmentation step based on level-set. The role of the registration and the classification is to initialize accurately the active model and to control its evolution. A hybrid region-boundary model using level set technique is also proposed. Our formalism is robust to noise and intensity inhomogeneities, since it exploits the advantage of the combination. It is evaluated on both simulated and real data, and compared with existing segmentation techniques. Qualitative and quantitative results demonstrated its performance and robustness.