Elements of information theory
Elements of information theory
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-modal Image Registration by Minimising Kullback-Leibler Distance
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
The Journal of Machine Learning Research
Compact representations for fast nonrigid registration of medical images
Compact representations for fast nonrigid registration of medical images
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Multi-modal image registration using dirichlet-encoded prior information
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
Likelihood maximization approach to image registration
IEEE Transactions on Image Processing
Bayesian Registration via Local Image Regions: Information, Selection and Marginalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Bayesian characterization of uncertainty in multi-modal image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Non-rigid image registration using gaussian mixture models
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Feature-based alignment of volumetric multi-modal images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Bayesian estimation of regularization and atlas building in diffeomorphic image registration
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.