Technical Program

Paper Detail

Paper:	SA-AM-OS2.7
Session:	Image Segmentation and Shape Analysis
Time:	Saturday, April 8, 11:50 - 12:10
Presentation:	Oral
Title:	Adaptive Graph Cuts with Tissue Priors for Brain MRI Segmentation
Authors:	Zhuang Song; University of Pennsylvania
	Nicholas Tustison; University of Pennsylvania
	Brian Avants; University of Pennsylvania
	James Gee; University of Pennsylvania
Abstract:	We describe a novel framework for automatic brain MRI tissue segmentation. To overcome inherent difficulties associated with this particular segmentation problem, we use a graph cut/atlas-based registration methodology optimized within an iterative mode. The basic graph cut algorithm guarantees a global or near-global minimum of an energy function associated with a Markov Random Field (MRF). For atlas-based graph cuts, we tailor this energy function to incorporate both {\em a priori} information derived from registered brain atlases as well as region and boundary information derived directly from the images. The iterative algorithm adaptively alternates segmentation and inhomogeneity correction. The proposed method can be extended to multispectral image segmentation. We validate our method in both simulated adult and real neonatal brain MR images corrupted by significant noise and intensity inhomogeneities.

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