PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning the Similarity Measure for Multi-Modal 3D Image Registration
Daewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan Cahill and Bernhard Scholkopf
In: CVPR 2009(2009).


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 for example 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 learning framework adapts to the specific registration problem at hand, it can exploit correlations between neighboring pixels in the reference and the floating image, and we demonstrate its superior and robust performance on difficult CT-MR/PET-MR rigid registration tasks.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6117
Deposited By:Yasemin Altun
Deposited On:08 March 2010