Canonical correlation analysis of sub-cortical brain structures using non-rigid registration

Authors:
Anil Rao;Kola Babalola;Daniel Rueckert
Affiliations:
Visual Information Processing Group, Department of Computing, Imperial College London, London, U.K;Division of Image Science & Bio-medical Engineering, University of Manchester, Manchester, U.K;Visual Information Processing Group, Department of Computing, Imperial College London, London, U.K
Venue:
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Year:
2006

Citing 6
Cited 0

Multiresolution elastic matching

Computer Vision, Graphics, and Image Processing
Active shape models—their training and application

Computer Vision and Image Understanding
Computational anatomy: an emerging discipline

Quarterly of Applied Mathematics - Special issue on current and future challenges in the applications of mathematics
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Individualizing Anatomical Atlases of the Head

VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Fast Fluid Registration of Medical Images

VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present the application of canonical correlation analysis to investigate how the shapes of different structures within the brain vary statistically relative to each other. Canonical correlation analysis is a multivariate statistical technique which extracts and quantifies correlated behaviour between two sets of vector variables. Firstly, we perform non-rigid image registration of 93 sets of 3D MR images to build sets of surfaces and correspondences for sub-cortical structures in the brain. Canonical correlation analysis is then used to extract and quantify correlated behaviour in the shapes of each pair of surfaces. The results show that correlations are strongest between neighbouring structures and reveal symmetry in the correlation strengths for the left and right sides of the brain.