A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard technique for computing such descriptions is principal component analysis. However, principal component analysis is limited in that it only works for data lying in a Euclidean vector space. While this is certainly sufficient for geometric models that are parameterized by a set of landmarks or a dense collection of boundary points, it does not handle more complex representations of shape. We have been developing representations of geometry based on the medial axis description or m-rep. While the medial representation provides a rich language for variability in terms of bending, twisting, and widening, the medial parameters are not elements of a Euclidean vector space. They are in fact elements of a nonlinear Riemannian symmetric space. In this paper, we develop the method of principal geodesic analysis, a generalization of principal component analysis to the manifold setting. We demonstrate its use in describing the variability of medially-defined anatomical objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.

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Abstract

In the paper, a general thin plate theory including surface effects, which can be used for size-dependent static and dynamic analysis of plate-like thin film structures, is proposed. This theory is a modification and generalization of the thin plate model in [Lim, C.W., He, L.H., 2004. Size-dependent nonlinear response of thin elastic films with nanoscale thickness. Int. J. Mech. Sci. 46, 1715-1726]. With the general theory, the governing equations of Kirchoff and Mindlin plate models including surface effects are derived, respectively. Some numerical examples are provided to verify the validities of the theory.