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Learning Similarity Measure for Multi-Modal 3D Image Registration

2009

Conference Paper

ei


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.

Author(s): Lee, D. and Hofmann, M. and Steinke, F. and Altun, Y. and Cahill, ND. and Schölkopf, B.
Book Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages: 186-193
Year: 2009
Month: June
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/CVPRW.2009.5206840
Event Name: CVPR 2009
Event Place: Miami, FL, USA

Address: Piscataway, NJ, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5777,
  title = {Learning Similarity Measure for Multi-Modal 3D Image Registration},
  author = {Lee, D. and Hofmann, M. and Steinke, F. and Altun, Y. and Cahill, ND. and Sch{\"o}lkopf, B.},
  booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  pages = {186-193},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2009},
  month_numeric = {6}
}