Technical Program

Paper Detail

Paper:	TH-PM-OS1.4
Session:	Atlases and Nonrigid Image Registration
Time:	Thursday, April 6, 17:40 - 18:00
Presentation:	Oral
Title:	Automatic Landmark Tracking Applied to Optimize Brain Conformal Mapping
Authors:	Lok Ming Lui; University of California, Los Angeles
	Yalin Wang; University of California, Los Angeles
	Tony F. Chan; University of California, Los Angeles
	Paul M. Thompson; UCLA School of Medicine
Abstract:	Important anatomical features on the cortical surface are usually represented by landmark curves, called sulci/gyri curves. Manual labelling of these landmark curves is time-consuming, especially when there is a large set of data. In this paper, we propose a method to trace the landmark curves on the cortical surfaces automatically based on the principal directions. Suppose we are given the global conformal parametrization of the cortical surface, our method traces the landmark curves iteratively on the spherical/rectangular parameter domain along the principal direction. Consequently, the landmark curves can be mapped onto the cortical surface. To speed up the iterative scheme, we propose a method to get a good initialization by extracting the high curvature region on the cortical surface using Chan-Vese segmentation method, which involves solving a PDE on the manifold using our global conformal parametrization technique. Experimental results show that the landmark curves detected by our algorithm closely resemble to those manually labelled curves. As an application, we used these automatically labelled landmark curves to build average cortical surfaces with an optimized brain conformal mapping method. Experimental results show our method can help automatically matching brain cortical surfaces.

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