Segmentation and Measurement of the Cortex from D MR Images

The cortex is the outermost thin layer of gray matter in the brain geometric measurement of the cortex helps in understanding brain anatomy and function In the quantitative analysis of the cortex from MR images extracting the structure and obtaining a representation for various measurements are key steps While manual segmentation is tedious and labor intensive automatic reliable and e cient segmenta tion and measurement of the cortex remain challenging problems due to its convoluted nature A new approach of coupled surfaces propagation using level set methods is presented here for the problem of the seg mentation and measurement of the cortex Our method is motivated by the nearly constant thickness of the cortical mantle and takes this tight coupling as an important constraint By evolving two embedded surfaces simultaneously each driven by its own image derived information while maintaining the coupling a nal representation of the cortical bound ing surfaces and an automatic segmentation of the cortex are achieved Characteristics of the cortex such as cortical surface area surface curva ture and thickness are then evaluated The level set implementation of surface propagation o ers the advantage of easy initialization computa tional e ciency and the ability to capture deep folds of the sulci Results and validation from various experiments on simulated and real D MR images are provided