Projects:BrainManifold

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Brain Manifold Learning

Manifold learned from OASIS database. The image shows a 2-dimensional parametrization of the database. The green, red and blue are the mean, median and mode images computed using the manifold representation

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.

Key Investigators

• Utah: Samuel Gerber, Tolga Tasdizen, Sarang Joshi, Tom Fletcher, Ross Whitaker

Publications

In Print Published in MICCAI and ICCV

• Manifold Learning Research Page
• NA-MIC Publications Database

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