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Conference Paper

Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration

MPS-Authors
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Hofmann,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bezrukov,  I
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Hofmann, M., Schölkopf, B., Bezrukov, I., & Cahill, N. (2009). Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration. In W. Wells, S. Joshi, & K. Pohl (Eds.), MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis (PMMIA 2009) (pp. 220-231).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C30D-6
Abstract
We present a methodology for incorporating prior knowledge on class probabilities into the registration process. By using knowledge
from the imaging modality, pre-segmentations, and/or probabilistic atlases,
we construct vectors of class probabilities for each image voxel. By
defining new image similarity measures for distribution-valued images,
we show how the class probability images can be nonrigidly registered in
a variational framework. An experiment on nonrigid registration of MR
and CT full-body scans illustrates that the proposed technique outperforms
standard mutual information (MI) and normalized mutual information
(NMI) based registration techniques when measured in terms of
target registration error (TRE) of manually labeled fiducials.