NDLI: Hierarchical Statistical Shape Analysis and Prediction of Sub-Cortical Brain Structures

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Combining Regions and Patches for Object Class Localization

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Automatic Image Quality Assessment with Application in Biometrics

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Hierarchical Statistical Shape Analysis and Prediction of Sub-Cortical Brain Structures

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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

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2003 Conference on Computer Vision and Pattern Recognition Workshop

1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

Hierarchical Statistical Shape Analysis and Prediction of Sub-Cortical Brain Structures

Content Provider	IEEE Xplore Digital Library
Author	Anil Rao Cootes, T. Rueckert, D.
Copyright Year	2006
Description	Author affiliation: Imperial College London, U.K. (Anil Rao)
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.
Starting Page	75
Ending Page	75
File Size	349231
Page Count	1
File Format	PDF
ISBN	0769526462
DOI	10.1109/CVPRW.2006.93
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Neuroscience Magnetic resonance Humans Brain modeling Educational institutions Active shape model Magnetic analysis Anatomy Least squares methods
Content Type	Text
Resource Type	Article

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