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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83974

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83809

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

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Zitation

Hofmann, M., Schölkopf, B., Bezrukov, I., & Cahill, N. (2009). Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration. Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis (PMMIA 2009), 220-231.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C30D-6
Zusammenfassung
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.