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

Stéphanie Allassonnière 1 Pierre Jolivet 1 Christophe Giraud 1
1 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
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.
Type de document :
Communication dans un congrès
Pennec, Xavier and Joshi, Sarang and Nielsen, Mads. 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, 2011
Liste complète des métadonnées

Littérature citée [25 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/inria-00623930
Contributeur : Xavier Pennec <>
Soumis le : jeudi 15 septembre 2011 - 14:05:31
Dernière modification le : mercredi 25 avril 2018 - 10:45:36
Document(s) archivé(s) le : vendredi 16 décembre 2011 - 02:25:06

Fichier

MFCA11_4_2.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00623930, version 1

Collections

X | X-CMAP | PARISTECH | MFCA | MFCA11 | CMAP

Citation

Stéphanie Allassonnière, Pierre Jolivet, Christophe Giraud. Detecting Long Distance Conditional Correlations Between Anatomical Regions Using Gaussian Graphical Models. Pennec, Xavier and Joshi, Sarang and Nielsen, Mads. 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, 2011. 〈inria-00623930〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

323

Téléchargements de fichiers

107

Contact


archive-ouverte@inria.fr