Ashburner, J;
Friston, KJ;
(2011)
Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation.
NEUROIMAGE
, 55
(3)
954 - 967.
10.1016/j.neuroimage.2010.12.049.
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Abstract
This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM). but with a more efficient optimisation scheme - both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data. (C) 2011 Elsevier Inc. All rights reserved.
| Type: | Article |
|---|---|
| Title: | Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2010.12.049 |
| Publisher version: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC322105... |
| Language: | English |
| Additional information: | Copyright © 2017 Elsevier B.V. or its licensors or contributors. This article is available under the terms of the Creative Commons Attribution License (CC BY). (https://creativecommons.org/licenses/by/3.0/) You may distribute and copy the article, create extracts, abstracts, and other revised versions, adaptations or derivative works of or from an article (such as a translation), to include in a collective work (such as an anthology), to text or data mine the article, including for commercial purposes without permission from Elsevier. The original work must always be appropriately credited. Permission is not required for this type of reuse. |
| Keywords: | Diffeomorphisms, Geodesic shooting, Shape modelling, Nonlinear registration, Gauss-Newton optimisation, Image Registration, Brain, Algorithm |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/702878 |
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