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MICCAI
2009
Springer

On the Manifold Structure of the Space of Brain Images

8 years 11 months ago
On the Manifold Structure of the Space of Brain Images
This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the set of images. The approach builds on recent advances in manifold learning that allow to uncover nonlinear trends in data. We combine this manifold learning with distance measures between images that capture shape, in order to learn the underlying structure of a database of brain images. The proposed method is generative. New images can be created from the manifold parametrization and existing images can be projected onto the manifold. By measuring projection distance of a held out set of brain images we evaluate the fit of the proposed manifold model to the data and we can compute statistical properties of the data using this mani...
Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ro
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2009
Where MICCAI
Authors Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ross T. Whitaker
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