NDLI: Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces

Content Provider	IEEE Xplore Digital Library
Author	Rongjie Lai Yonggang Shi Sicotte, N. Toga, A.W.
Copyright Year	2011
Description	Author affiliation: Department of Neurology, University of California, Los Angeles, USA (Yonggang Shi; Toga, A.W.) \|\| Department of Mathematics, University of Southerm California, USA (Rongjie Lai) \|\| Cedar Sinai Medical Center, Los Angeles, U. S. A. (Sicotte, N.)
Abstract	Corpus callosum (CC) is an important structure in human brain anatomy. In this work, we propose a fully automated and robust approach to extract corpus callosum from T1-weighted structural MR images. The novelty of our method is composed of two key steps. In the first step, we find an initial guess for the curve representation of CC by using the zero level set of the first nontrivial Laplace-Beltrami (LB) eigenfunction on the white matter surface. In the second step, the initial curve is deformed toward the final solution with a geodesic curvature flow on the white matter surface. For numerical solution of the geodesic curvature flow on surfaces, we represent the contour implicitly on a triangular mesh and develop efficient numerical schemes based on finite element method. Because our method depends only on the intrinsic geometry of the white matter surface, it is robust to orientation differences of the brain across population. In our experiments, we validate the proposed algorithm on 32 brains from a clinical study of multiple sclerosis disease and demonstrate that the accuracy of our results.
Starting Page	2034
Ending Page	2040
File Size	1098838
Page Count	7
File Format	PDF
ISBN	9781457711015
ISSN	15505499
e-ISBN	9781457711022
DOI	10.1109/ICCV.2011.6126476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Eigenvalues and eigenfunctions Robustness Geometry Sparse matrices Image segmentation Multiple sclerosis Symmetric matrices
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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