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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Brun, C.C. Lepore, N. Pennec, X. Yi-Yu Chou Lee, A.D. Barysheva, M. de Zubicaray, G.I. McMahon, K.L. Wright, M.J. Toga, A.W. Thompson, P.M. |
| Copyright Year | 2009 |
| Description | Author affiliation: Centre for Magnetic Resonance, University of Queensland, Brisbane, 4072, Australia (de Zubicaray, G.I.; McMahon, K.L.) || Laboratory of Neuro Imaging, Department of Neurology, UCLA, Los Angeles, CA 90095, USA (Brun, C.C.; Lepore, N.; Yi-Yu Chou; Lee, A.D.; Barysheva, M.; Toga, A.W.; Thompson, P.M.) || Genetic Epidemiology Lab, Queensland Institute of Medical Research, 4029, Australia (Wright, M.J.) || Asclepios Research Project, INRIA, 06902 Sophia-Antipolis Cedex, France (Pennec, X.) |
| Abstract | We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate $0^{th}$ and $1^{st}$ order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms. |
| Starting Page | 975 |
| Ending Page | 978 |
| File Size | 301414 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193217 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Genetics Tensile stress Mathematical model Equations Covariance matrix Strain genetics registration statistical prior Riemannian metrics |
| Content Type | Text |
| Resource Type | Article |
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