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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.