Canonical Correlation Analysis of Sub-cortical Brain Structures Using Non-rigid Registration

  • Anil Rao
  • Kola Babalola
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)


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.


Lateral Ventricle Canonical Correlation Analysis Surface Point Magnetic Resonance Spectroscopic Image Correlate Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anil Rao
    • 1
  • Kola Babalola
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Visual Information Processing Group, Department of ComputingImperial College LondonLondonU.K
  2. 2.Division of Image Science & Bio-medical EngineeringUniversity of ManchesterManchesterU.K

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