Segmentation of 3D Brain Structures in MRI Images

Abstract

The main topic of this paper is to segment brain tumors, their components (edema and necrosis) and internal structures of the brain in 3D MR images. For tumor segmentation, we propose a framework that is a combination of region-based and boundary-based paradigms. In this framework, we first segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetry based histogram analysis. The second step segments the tumor and its components. For this, we propose a new and original method that combines region and boundary information in two phases: initialization and refinement. For initialization, which is mostly region-based, we present two new methods. The first one is a new fuzzy classification method which combines the membership, typicality and neighborhood information of the voxels. The second one relies on symmetry-based histogram analysis. The initial segmentation of the tumor is refined relying on boundary information of the image. This method is a deformable model constrained by spatial relations. The spatial relations are obtained based on the initial segmentation and surrounded tissues of the tumor. The proposed method can be used for a large class of tumors in any modality of MR images. To segment a tumor and its components automatically the proposed framework needs only a contrast enhanced T1-weighted image and a FLAIR image. In the case of a contrast enhanced T1-weighted image only, some user interaction will be needed. To refine the initial segmentation, we use a deformable model which is again constrained by the fused spatial relations of the structure. This method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles, caudate nucleus and thalamus.