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

2009

Conference Paper

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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.

Author(s): Hofmann, M. and Schölkopf, B. and Bezrukov, I. and Cahill, ND.
Book Title: Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis
Pages: 220-231
Year: 2009
Month: September
Day: 0
Editors: W Wells and S Joshi and K Pohl

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: PMMIA 2009
Event Place: London, UK

Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6040,
  title = {Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration},
  author = {Hofmann, M. and Sch{\"o}lkopf, B. and Bezrukov, I. and Cahill, ND.},
  booktitle = {Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis  },
  pages = {220-231},
  editors = {W Wells and S Joshi and K Pohl},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2009},
  month_numeric = {9}
}