Population Shape Regression from Random Design Data
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Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. When the underlying process can be modeled by parameters in a Euclidean space, classical regression techniques (Hardle, Applied Nonparametric Regression, 1990; Wand and Jones, Kernel Smoothing, 1995) are applicable and have been studied extensively. However, recent work suggests that attempts to describe anatomical shapes using flat Euclidean spaces undermines our ability to represent natural biological variability (Fletcher et al., IEEE Trans. Med. Imaging 23(8), 995–1005, 2004; Grenander and Miller, Q. Appl. Math. 56(4), 617–694, 1998).
In this paper we develop a method for regression analysis of general, manifold-valued data. Specifically, we extend Nadaraya-Watson kernel regression by recasting the regression problem in terms of Fréchet expectation. Although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data.
We demonstrate our method by analyzing shape change in the brain from a random design dataset of MR images of 97 healthy adults ranging in age from 20 to 79 years. To study the small scale changes in anatomy, we use the infinite dimensional manifold of diffeomorphic transformations, with an associated metric. We regress a representative anatomical shape, as a function of age, from this population.
KeywordsSpatio-temporal shape analysis Kernel regression Deformable atlas building
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- Beg, M., Miller, M., Trouvé, A., & Younes, L. (2005). Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision, 61(2). Google Scholar
- Beg, M. F. (2003). Variational and computational methods for flows of diffeomorphisms in image matching and growth in computational anatomy. PhD thesis, The Johns Hopkins University. Google Scholar
- Fréchet, M. (1948). Les elements aleatoires de nature quelconque dans un espace distancie. Annales de L’Institut Henri Poincare, 10, 215–310. Google Scholar
- Guttmann, C., Jolesz, F., Kikinis, R., Killiany, R., Moss, M., Sandor, T., & Albert, M. (1998). White matter changes with normal aging. Neurology, 50(4), 972–978. Google Scholar
- Hardle, W. (1990). Applied nonparametric regression. Cambridge: Cambridge University Press. Google Scholar
- Kendall, D. G. (1984). Shape manifolds, Procrustean metrics, and complex projective spaces. Bulletin of the London Mathematical Society, 16, 18–121. Google Scholar
- Mortamet, B., Zeng, D., Gerig, G., Prastawa, M., & Bullitt, E. (2005). Effects of healthy aging measured by intracranial compartment volumes using a designed mr brain database. In Lecture notes in computer science (LNCS): Vol. 3749. Medical image computing and computer assisted intervention (MICCAI) (pp. 383–391). Google Scholar
- Wand, M. P., & Jones, M. C. (1995). Kernel smoothing : Vol. 60. Monographs on statistics and applied probability. London: Chapman & Hall/CRC. Google Scholar