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Detecting Long Distance Conditional Correlations Between Anatomical Regions Using Gaussian Graphical Models

Abstract : The conditional correlation patterns of an anatomical shape may provide some important information on the structure of this shape. We propose to investigate these patterns by Gaussian Graphical Modelling. We design a model which takes into account both local and long-distance dependencies. We provide an algorithm which estimates sparse long-distance conditional correlations, highlighting the most significant ones. The selection procedure is based on a criterion which quantifies the quality of the conditional correlation graph in terms of prediction. The preliminary results on AD versus control population show noticeable differences.
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https://hal.inria.fr/inria-00623930
Contributor : Xavier Pennec <>
Submitted on : Thursday, September 15, 2011 - 2:05:31 PM
Last modification on : Friday, June 5, 2020 - 6:54:05 AM
Document(s) archivé(s) le : Friday, December 16, 2011 - 2:25:06 AM

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  • HAL Id : inria-00623930, version 1

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Stéphanie Allassonnière, Pierre Jolivet, Christophe Giraud. Detecting Long Distance Conditional Correlations Between Anatomical Regions Using Gaussian Graphical Models. Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability, Sep 2011, Toronto, Canada. pp.111-122. ⟨inria-00623930⟩

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