An Automatic MRI Brain Segmentation by Using Adaptive Mean-Shift Clustering Framework

Abstract

A novel fully, automatic, adaptive, robust procedure for brain tissue classification from three-dimensional (3D) magnetic resonance head images (MRI) is described in this paper. We propose an automated scheme for magnetic resonance imaging (MRI) brain segmentation. An adaptive mean-shift methodology is utilized in order to categorize brain voxels into one of three main tissue types: gray matter, white matter, and cerebro spinal fluid. The MRI image space is characterized by a high dimensional feature space that includes multimodal intensity features in addition to spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better applicant for intensity based categorization than the initial voxels. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.