Statistics on diffeomorphisms via tangent space representations.

M Vaillant; M I Miller; L Younes; A Trouvé

doi:10.1016/j.neuroimage.2004.07.023

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Statistics on diffeomorphisms via tangent space representations.

M Vaillant·M I Miller·L Younes·A Trouvé

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Abstract

In this paper, we present a linear setting for statistical analysis of shape and an optimization approach based on a recent derivation of a conservation of momentum law for the geodesics of diffeomorphic flow. Once a template is fixed, the space of initial momentum becomes an appropriate space for studying shape via geodesic flow since the flow at any point along the geodesic is completely determined by the momentum at the origin through geodesic shooting equations. The space of initial momentum provides a linear representation of the nonlinear diffeomorphic shape space in which linear statistical analysis can be applied. Specializing to the landmark matching problem of Computational Anatomy, we derive an algorithm for solving the variational problem with respect to the initial momentum and demonstrate principal component analysis (PCA) in this setting with three-dimensional face and hippocampus databases.

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Algorithms Anatomy Computational Biology Databases, Factual Face Humans Linear Models Models, Anatomic Models, Statistical Principal Component Analysis Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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M Vaillant, M I Miller, L Younes, A Trouvé. 2004. Statistics on diffeomorphisms via tangent space representations.. https://doi.org/10.1016/j.neuroimage.2004.07.023

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