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May  2007, 7(3): 605-618. doi: 10.3934/dcdsb.2007.7.605

Brain anatomical feature detection by solving partial differential equations on general manifolds

Lok Ming Lui 1, , Yalin Wang 1, , Tony F. Chan 2, and  Paul M. Thompson 3,

1. 

Department of Mathematics, UCLA, Los Angeles, CA 90095-1555, United States, United States
2. 

Department of Mathematics, UCLA, 520 Portola Plaza, Math Sciences Building 6363, Los Angeles, CA 90095, United States
3. 

Laboratory of Neuro Imaging and Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-7334, United States

Received  September 2006 Revised  January 2007 Published  February 2007

One important problem in human brain mapping research is to locate the important anatomical features. Anatomical features on the cortical surface are usually represented by landmark curves, called sulci/gyri curves. These landmark curves are important information for neuroscientists to study brain disease and to match different cortical surfaces. Manual labelling of these landmark curves is time-consuming, especially when large sets of data have to be analyzed. In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an algorithm to obtain a hypothesized landmark region/curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. This is done by segmentating the high mean curvature region. Second, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.
Keywords: Conformal Parametrization, Partial Differential Equations, Brain Anatomical Feature, Curvature., Variational problem.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Lok Ming Lui, Yalin Wang, Tony F. Chan, Paul M. Thompson. Brain anatomical feature detection by solving partial differential equations on general manifolds. Discrete & Continuous Dynamical Systems - B, 2007, 7 (3) : 605-618. doi: 10.3934/dcdsb.2007.7.605
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