Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration
MICCAI '99 Proceedings of the Second 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
Accurate and precise 2D-3D registration based on X-ray intensity
Computer Vision and Image Understanding
Bayesian Registration via Local Image Regions: Information, Selection and Marginalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Generalizing Inverse Compositional and ESM Image Alignment
International Journal of Computer Vision
Multi-modal image registration using dirichlet-encoded prior information
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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Image similarity measures for registration can be considered within the general context of joint intensity histograms, which consist of bin count parameters estimated from image intensity samples. Many approaches to estimation are ML (maximum likelihood), which tends to be unstable in the presence sparse data, resulting in registration that is driven by spurious noisy matches instead of valid intensity relationships. We propose instead a method of MAP (maximum a posteriori) estimation, which is well-defined for sparse data, or even in the absence of data. This estimator can incorporate a variety of prior assumptions, such as global histogram characteristics, or use a maximum entropy prior when no such assumptions exist. We apply our estimation method to deformable registration of MR (magnetic resonance) and US (ultrasound) images for an IGNS (image-guided guided neurosurgery) application, where our MAP estimation method results in more stable and accurate registration than a traditional ML approach.