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Brain Manifold Learning
This work investigates the use of manifold learning approaches in the context of brain population analysis. The goal is to construct a manifold model from a set of brain images that captures variability in shape, a parametrization of the shape space. Such a manifold model is interesting in several ways
- The low dimensional parametrization simplifies statistical analysis of populations.
- Applications to searching and browsing large database
- The manifold represents a localized Atlas. Alternative to template based applications, for example as a segmentation prior.
- Aid in clinical diagnosis. Different regions on the manifold can indicate different pathologies.
Description
Key Investigators
- Utah: Samuel Gerber, Tolga Tasdizen, Sarang Joshi, Tom Fletcher, Ross Whitaker
Publications
In Print Published in MICCAI and ICCV
In Press
- S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009
- S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009