Submitted by daewonlee on 2009, April 27 - 04:26.746 views | 0 comments | 6 votes
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Learning Similarity Measure for Multi-Modal 3D Image Registration

  • Learning Similarity Measure for Multi-Modal 3D Image Registration
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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.
Daewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan Cahill, Bernhard Schslkopf
CVPR - 2009
IEEE

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Keywords computer vision | CVPR 2009 | Medical image registration | similarity measure | structured output prediction |
Category Biomedical | Computer Science | Engineering |


Added 27 Apr 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Daewon Lee (Max Planck Institute for Biological Cybernetics), Matthias Hofmann (Max Planck Institute for Biological Cybernetics), Florian Steinke (Siemens Corporate Technology), Yasemin Altun (Max Planck Institute for Biological Cybernetics), Nathan Cahill (University of Oxford), Bernhard Schslkopf (Max Planck Institute for Biological Cybernetics)

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