Conditional Variability of Statistical Shape Models Based on Surrogate Variables

R. Blanc 1 Mauricio Reyes 2 Christof Seiler 2, 3 G. Székely 1
1 Computer Vision Laboratory - ETHZ [Zurich]
2 ISTB - Institute for Surgical Technology and Biomechanics [Bern]
3 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.
Type de document :
Communication dans un congrès
Proceedings of Medical Image Computing and Computer Assisted Intervention 2009 (MICCAI), 2009, London, United Kingdom. Springer, Heidelberg, 5762, pp.84-91, 2009, LNCS
Liste complète des métadonnées
https://hal.inria.fr/hal-00813743
Contributeur : Project-Team Asclepios <>
Soumis le : mardi 16 avril 2013 - 11:00:00
Dernière modification le : jeudi 11 janvier 2018 - 16:43:46

Identifiants

  • HAL Id : hal-00813743, version 1

Collections

INRIA | TDS-MACS | UNIV-COTEDAZUR

Citation

R. Blanc, Mauricio Reyes, Christof Seiler, G. Székely. Conditional Variability of Statistical Shape Models Based on Surrogate Variables. Proceedings of Medical Image Computing and Computer Assisted Intervention 2009 (MICCAI), 2009, London, United Kingdom. Springer, Heidelberg, 5762, pp.84-91, 2009, LNCS. 〈hal-00813743〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

122

Contact


archive-ouverte@inria.fr