@inbook {IOPORT.05730348,
    author       = {Mertzanidou, Thomy and Hipwell, John H. and Cardoso, M.Jorge and Tanner, Christine and Ourselin, Sebastien and Hawkes, David J.},
    title        = {X-ray mammography -- MRI registration using a volume-preserving affine transformation and an EM-MRF for breast tissue classification.},
    year         = {2010},
    booktitle    = {Digital mammography. 10th international workshop, IWDM 2010, Girona, Catalonia, Spain, June 16--18, 2010. Proceedings},
    isbn         = {978-3-642-13665-8},
    pages        = {23-30},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-13666-5_4},
    abstract     = {Summary: Registration of MR volumes to X-ray mammograms is a clinically valuable task, as each modality provides complementary information on normal and abnormal breast tissue structure and function. We propose an intensity-based technique with a 3D volume-preserving affine transformation. An important part of our framework is the use of an Expectation-Maximization (EM) algorithm, with a Markov Random Field (MRF) regularization, that is used for breast tissue classification and subsequently the mapping of the MR intensities to X-ray attenuation. Initially, the proposed framework was tested on simulated X-ray data, where the goal was to register the original undeformed MRI to a simulated X-ray that was produced using a real compression image, acquired from volunteers in the MR scanner (8 cases). Since the ground truth in this case can be estimated from individually defined landmarks, we have evaluated the mean reprojection error, which was $3.83$mm. The algorithm was then applied and evaluated visually on 5 cases that had both X-ray mammograms and MRIs.},
    identifier   = {05730348},
}