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  Learning the Similarity Measure for Multi-Modal 3D Image Registration

Lee, D., Hofmann, M., Steinke, F., Altun, Y., Cahill, N., & Schölkopf, B. (2009). Learning the Similarity Measure for Multi-Modal 3D Image Registration. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 186-193). Piscataway, NJ, USA: IEEE Service Center.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C48D-B Version Permalink: http://hdl.handle.net/21.11116/0000-0002-F8FC-B
Genre: Conference Paper

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 Creators:
Lee, D1, 2, Author              
Hofmann, M1, 2, Author              
Steinke, F1, 2, Author              
Altun, Y1, 2, Author              
Cahill, ND, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CT-MR/PET-MR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multi-modal medical image registration.

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 Dates: 2009-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/CVPRW.2009.5206840
BibTex Citekey: 5777
 Degree: -

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Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)
Place of Event: Miami, FL, USA
Start-/End Date: 2009-06-20 - 2009-06-25

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Title: 2009 IEEE Conference on Computer Vision and Pattern Recognition
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 186 - 193 Identifier: ISBN: 978-1-4244-3991-1