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