Membership-based multiprotocol MR brain image segmentation

Abstract

A simple,non-iterative,membership-based method for multiprotocol brain magnetic resonance image segmentation has been developed. The intensity in homogeneity correction and MR intensity standardization techniques are used first to make the MR image intensities have a tissue-specific meaning. The mean intensity vector and covariance matrix of each brain tissue are then estimated and fixed. Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized to geernate the brain intracranial mask. The fuzzy membership value of each voxel for each brain tissue is then estimated within the intracranial mask via a multivariate Gaussian model. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into gray matter, white matter, and cerebrospinal fluid. This method has been tested on 10 clinical MR data sets. These tests and a comparison with the method of C-means and fuzzy C-means clustering indicated the effectiveness of the method.