Non-local graph-based regularization for deformable image registration
Papiez BW, Szmul A, Grau V, Brady JM, Schnabel JA (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10081 LNCS
Pages Range: 199-207
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Athens, GRC
ISBN: 9783319611877
DOI: 10.1007/978-3-319-61188-4_18
Abstract
Deformable image registration aims to deliver a plausible spatial transformation between two or more images by solving a highly dimensional, ill-posed optimization problem. Covering the complexity of physiological motion has so far been limited to either generic physical models or local motion regularization models. This paper presents an alternative, graphical regularization model, which captures well the non-local scale of motion, and thus enables to incorporate complex regularization models directly into deformable image registration. In order to build the proposed graph-based regularization, a Minimum Spanning Tree (MST), which represents the underlying tissue physiology in a perceptually meaningful way, is computed first. This is followed by a fast non-local cost aggregation algorithm that performs regularization of the estimated displacement field using the precomputed MST. To demonstrate the advantage of the presented regularization, we embed it into the widely used Demons registration framework. The presented method is shown to improve the accuracy for exhale-inhale CT data pairs.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Papiez, B.W., Szmul, A., Grau, V., Brady, J.M., & Schnabel, J.A. (2017). Non-local graph-based regularization for deformable image registration. In Tal Arbel, Georg Langs, Mark Jenkinson, Bjoern Menze, William M. Wells III, Albert C.S. Chung, B. Michael Kelm, Weidong Cai, Albert Montillo, Dimitris Metaxas, M. Jorge Cardoso, Shaoting Zhang, Annemie Ribbens, Henning Muller (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 199-207). Athens, GRC: Springer Verlag.
MLA:
Papiez, Bartlomiej W., et al. "Non-local graph-based regularization for deformable image registration." Proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, GRC Ed. Tal Arbel, Georg Langs, Mark Jenkinson, Bjoern Menze, William M. Wells III, Albert C.S. Chung, B. Michael Kelm, Weidong Cai, Albert Montillo, Dimitris Metaxas, M. Jorge Cardoso, Shaoting Zhang, Annemie Ribbens, Henning Muller, Springer Verlag, 2017. 199-207.
BibTeX: Download