Fast Deformable Image Registration with Non-smooth Dual Optimization

Rajchl M, Baxter JSH, Qiu W, Khan AR, Fenster A, Peters TM, Rueckert D, Yuan J (2016)

Publication Type: Conference contribution

Publication year: 2016

Publisher: IEEE Computer Society

Pages Range: 465-472

Conference Proceedings Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Event location: Las Vegas, NV, USA

ISBN: 9781467388504

DOI: 10.1109/CVPRW.2016.65

Abstract

Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms. These regularization mechanisms are designed to mitigate the effects of trivial solutions to ill-posed registration problems and to otherwise ensure the resulting deformation fields are well-behaved. This paper introduces a novel deformable registration (DR) algorithm, RANCOR, which uses iterative convexification to address DR problems under nonsmooth total-variation regularization. Initial comparative results against four state-of-the-art registration algorithms and under smooth regularization, respectively, are presented using the Internet Brain Segmentation Repository (IBSR) database.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Western University CA Canada (CA)

How to cite

APA:

Rajchl, M., Baxter, J.S.H., Qiu, W., Khan, A.R., Fenster, A., Peters, T.M.,... Yuan, J. (2016). Fast Deformable Image Registration with Non-smooth Dual Optimization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 465-472). Las Vegas, NV, USA: IEEE Computer Society.

MLA:

Rajchl, Martin, et al. "Fast Deformable Image Registration with Non-smooth Dual Optimization." Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016, Las Vegas, NV, USA IEEE Computer Society, 2016. 465-472.

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