The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Point similarity measures for non-rigid registration of multi-modal data
Computer Vision and Image Understanding
A marginalized MAP approach and EM optimization for pair-wise registration
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Summarizing and visualizing uncertainty in non-rigid registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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Understanding and quantifying the uncertainty involved when registering images is an important problem in medical imaging, where clinical decisions are made based on the registered solution. This is especially important in non-rigid registration where the higher degrees of freedom may provide unwarranted confidence in the results, through over-fitting. The Bayesian approach, which defines uncertainty as the posterior distribution on deformations, requires a generative model of the image formation process where the fixed image is modeled as a deformed version of the moving image plus a noise term. As per this model, the likelihood term is equivalent to the sum-of-squared differences image matching metric and is therefore valid only for same-mode image registration. In this paper, we propose a general formalism to quantify Bayesian uncertainty in the registration of multi-modal images through an extended probability model that introduces and then marginalizes out a stochastic transfer function between moving and fixed image intensities.