Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

Abstract : This paper presents a new framework for capturing large and complex deformations in image registration and atlas construction. This challenging and recurrent problem in computer vision and medical imaging currently relies on iterative and local approaches, which are prone to local minima and, therefore, limit present methods to relatively small deformations. Our general framework introduces to this effect a new direct feature matching technique that finds global correspondences between images via simple nearest-neighbor searches. More specifically, very large image deformations are captured in Spectral Forces, which are derived from an improved graph spectral representation. We illustrate the benefits of our framework through a new enhanced version of the popular Log-Demons algorithm, named the Spectral Log-Demons, as well as through a groupwise extension, named the Groupwise Spectral Log-Demons, which is relevant for atlas construction. The evaluations of these extended versions demonstrate substantial improvements in accuracy and robustness to large deformations over the conventional Demons approaches.
Type de document :
Article dans une revue
International Journal of Computer Vision, Springer Verlag, 2014, 107 (3), pp.254-271. 〈10.1007/s11263-013-0681-5〉
Liste complète des métadonnées

Littérature citée [72 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00979616
Contributeur : Lombaert Herve <>
Soumis le : mardi 18 novembre 2014 - 16:33:59
Dernière modification le : jeudi 11 janvier 2018 - 16:17:44
Document(s) archivé(s) le : jeudi 19 février 2015 - 12:06:38

Fichier

SpectralDemons-IJCV2013.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Marque du Domaine Public 4.0 International License

Identifiants

Collections

Citation

Herve Lombaert, Leo Grady, Xavier Pennec, Nicholas Ayache, Farida Cheriet. Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations. International Journal of Computer Vision, Springer Verlag, 2014, 107 (3), pp.254-271. 〈10.1007/s11263-013-0681-5〉. 〈hal-00979616〉

Partager

Métriques

Consultations de la notice

280

Téléchargements de fichiers

543