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Neuroimage. 2009 Mar;45(1 Suppl):S40-50. doi: 10.1016/j.neuroimage.2008.10.050. Epub 2008 Nov 12.

Evolutions equations in computational anatomy.

Author information

1
Center for Imaging Science, The Johns Hopkins University, 3400N Charles St., Baltimore, MD 21218, USA. laurent.younes@jhu.edu

Abstract

One of the main purposes in computational anatomy is the measurement and statistical study of anatomical variations in organs, notably in the brain or the heart. Over the last decade, our group has progressively developed several approaches for this problem, all related to the Riemannian geometry of groups of diffeomorphisms and the shape spaces on which these groups act. Several important shape evolution equations that are now used routinely in applications have emerged over time. Our goal in this paper is to provide an overview of these equations, placing them in their theoretical context, and giving examples of applications in which they can be used. We introduce the required theoretical background before discussing several classes of equations of increasingly complexity. These equations include energy minimizing evolutions deriving from Riemannian gradient descent, geodesics, parallel transport and Jacobi fields.

PMID:
19059343
PMCID:
PMC2650001
DOI:
10.1016/j.neuroimage.2008.10.050
[Indexed for MEDLINE]
Free PMC Article

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