View source for Projects:BrainManifold

 Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Brain Manifold Learning =
[[Image:sgerber_brainmanifold_oasis_manifold.png|thumb|300px|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.

= Description =
In many neuroimage applications a summary or representation of a population of brain images is needed. A common approach is to build a template, or atlas, that represents a population. Recent work introduced clustering based approaches, which in a data driven fashion, compute multiple templates  Each template represents a part of the population. In a different direction, researcher proposed kernel-based regression of brain images with respect to an underlying parameter. This yields a continuous curve in the space of brain images that estimates the conditional expectation of a brain image given the parameter.  A natural question that arises based on these investigations is can the space spanned by a set of brain images be approximated by a low-dimensional manifold? In other words, how effectively can a low-dimensional, nonlinear model represent the variability in brain anatomy.

= Key Investigators =

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

= Publications =

'' In Print ''
''Published in MICCAI and ICCV''
* [http://www.cs.utah.edu/~sgerber/research/ Manifold Learning Research Page]
* [http://www.na-mic.org/publications/pages/display?search=BrainManifold&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Brain Manifold Learning]

'' 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


[[Category:Statistics]] [[Category:Registration]]