Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation

The cortex is the outermost thin layer of gray matter in the brain; geometr ic measurement of the cortex helps in understanding brain anatomy and funct ion. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various measurements are k ey steps. While manual segmentation is tedious and labor intensive, automat ic reliable efficient segmentation and measurement of the cortex remain cha llenging problems, due to its convoluted nature. Here we present a new appr oach of coupled-surfaces propagation, using level set methods to address su ch problems, Our method is motivated by the nearly constant thickness of th e cortical mantle and takes this tight coupling as an important constraint. By evolving two embedded surfaces simultaneously, each driven by its own i mage-derived information while maintaining the coupling, a final representa tion of the cortical bounding surfaces and an automatic segmentation of the cortex are achieved. Characteristics of the cortex, such as cortical surfa ce area, surface curvature, and cortical thickness, are then evaluated, The level set implementation of surface propagation offers the advantage of ea sy initialization, computational efficiency, and the ability to capture dee p sulcal folds. Results and validation from various experiments on both sim ulated and real three-dimensional (3-D) MR images are provided.