Hierarchical statistical shape analysis and prediction of sub-cortical brain structures

Anil Rao, Tim Cootes, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we present the application of two multivariate statistical techniques to investigate how different structures within the brain vary statistically relative to each other. The first of these techniques is canonical correlation analysis which extracts and quantifies correlated behaviour between two sets of vector variables. The second technique is partial least squares regression which determines the best factors within a first set of vector variables for predicting a vector variable from a second set. We describe how these techniques can be used to quantify and predict correlated behaviour in sub-cortical structures within the brain using 3D MR images.

Original languageEnglish
Title of host publication2006 Conference on Computer Vision and Pattern Recognition Workshop
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States
Duration: 17 Jun 200622 Jun 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2006
ISSN (Print)1063-6919

Conference

Conference2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/TerritoryUnited States
CityNew York, NY
Period17/06/0622/06/06

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