Anatomy dependent multi-context fuzzy clustering for separation of brain tissues in MR images

Abstract

In a previous work, we proposed multi-context fuzzy clustering (MCFC) method on the basis of a local tissue distribution model to classify 3D T1-weighted MR images into tissues of white matter, gray matter, and cerebral spinal fluid in the condition of intensity inhomogeneity. This paper is a complementary and improved version of MCFC. Firstly, quantitative analyses are presented to validate the soundness of basic assumptions of MCFC. Carefully studies on the segmentation results of MCFC disclose a fact that misclassification rate in a context of MCFC is spatially dependent on the anatomical position of the context in the brain; moreover, misclassifications concentrate in regions of brain stem and cerebellum. Such unique distribution pattern of misclassification inspires us to choose different size for the contexts at such regions. This anatomy-dependent MCFC (adMCFC) is tested on 3 simulated and 10 clinical T1-weighted images sets. Our results suggest that adMCFC outperforms MCFC as well as other related methods. © Springer-Verlag 2004.