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Global image registration using a symmetric block-matching approach.

Modat, M; Cash, DM; Daga, P; Winston, GP; Duncan, JS; Ourselin, S; (2014) Global image registration using a symmetric block-matching approach. J Med Imaging (Bellingham) , 1 (2) , Article 024003. 10.1117/1.JMI.1.2.024003. Green open access

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Abstract

Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.

Type: Article
Title: Global image registration using a symmetric block-matching approach.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JMI.1.2.024003
Publisher version: http://dx.doi.org/10.1117/1.JMI.1.2.024003
Additional information: © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Keywords: Image registration, multimodal, robust, symmetry
UCL classification: UCL > Office of the President and Provost
UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Clinical and Experimental Epilepsy
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Neurodegenerative Diseases
UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1463769
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