TY - GEN
T1 - Non-local graph-based regularization for deformable image registration
AU - Papież, Bartłomiej W.
AU - Szmul, Adam
AU - Grau, Vicente
AU - Brady, J. Michael
AU - Schnabel, Julia A.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85025172333&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61188-4_18
DO - 10.1007/978-3-319-61188-4_18
M3 - Conference contribution
AN - SCOPUS:85025172333
SN - 9783319611877
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 207
BT - Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
A2 - Arbel, Tal
A2 - Langs, Georg
A2 - Jenkinson, Mark
A2 - Menze, Bjoern
A2 - Wells III, William M.
A2 - Chung, Albert C.S.
A2 - Kelm, B. Michael
A2 - Cai, Weidong
A2 - Montillo, Albert
A2 - Metaxas, Dimitris
A2 - Cardoso, M. Jorge
A2 - Zhang, Shaoting
A2 - Ribbens, Annemie
A2 - Muller, Henning
PB - Springer Verlag
T2 - 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
Y2 - 21 October 2016 through 21 October 2016
ER -