The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Multimodality image registration by maximization of mutual information
Open accessAbstract
A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
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“A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose [8, 14, 34].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Conventional intensity-based 2D/3D registration approaches suffer from a limited capture range due to the presence of local minima in hand-crafted image similarity functions.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While these metrics are positively correlated with pose differences when the perturbations in {K^v,T^xv,ω^D} are small, they are generally non-convex and fail to accurately represent pose offsets when perturbations are large.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Maes et al., 1997
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Conventional intensity-based methods mostly use hand-crafted similarity metrics between y v and its current best estimate y^v=(P(Kv,Txv)◦DωD)(x)fx1 as loss function. Common choices for the similarity metric are Normalized Cross Correlation (NCC), gradient information (Berger et al., 2016), or Mutual Information (MI) (Maes et al., 1997).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Maes et al., 1997
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Citations
Cites
Elements of Information Theory
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A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079
Multi-modal volume registration by maximization of mutual information
Wells, W. M., Viola, P. A., Atsumi, H., Nakajima, S., & Kikinis, R. (1996) · Medical Image Analysis · doi:10.1016/s1361-8415(01)80004-9 · Cited by 1,961
MRI-PET Registration with Automated Algorithm
Woods, R. P., Mazziotta, J. C., & Cherry, A. S. R. (1993) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199307000-00004 · Cited by 1,636
Alignment by maximization of mutual information
Viola, P. A., & Wells, W. M. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466930 · Cited by 1,080
Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques
West, J. B., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., KESSLER, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A. M. F., et al. (1997) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199707000-00007 · Cited by 883
Self-citationMulti-modality image registration by maximization of mutual information
Maes, F., Collignon, A. M. F., Vandermeulen, D., Marchal, G., & Suetens, P. (1996) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.1996.534053 · Cited by 809
Self-citation
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- collignon, 1995, automated multimodality medical image registration using information theory, Proc 14th Int Conf Information Processing in Medical Imaging Computational Imaging and Vision 3, 263
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Cited by
Sorted by citations
Items 1,001–1,006 of 1,006
A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers
Gao, C., Feng, A., Liu, X., Taylor, R. H., Armand, M., & Unberath, M. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3299588 · Cited by 36
“Conventional image intensity-based 2D/3D registration methods use hand-crafted image similarity functions to perform iterative pose optimization, such as mutual information (MI) [7] or patch-based gradient normalized cross correlation (P-Grad-NCC) [8].”
MethodSo, R. W. K., & Chung, A. C. S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.004 · Cited by 36
Regional brain activity during shape recognition impaired by a scopolamine challenge to encoding
Rosier, A., Cornette, L., Dupont, P., Bormans, G., Mortelmans, L., & Orban, G. A. (1999) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.1999.00784.x · Cited by 34
Accelerating image registration of MRI by GPU-based parallel computation
Huang, T.-Y., Tang, Y.-W., & Ju, S.-Y. (2011) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2011.02.027 · Cited by 32
Chakravorti, S., Jermakowicz, W. J., Wu, C., Li, R., Wirz Gonzalez, R., Dawant, B. M., & D'Haese, P.-F. (2019) · Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2512587 · Cited by 3
Akdemir, K., & Yanardag, P. (2024) · arXiv · doi:10.48550/arxiv.2406.02820 · Cited by 8
“Using mutual information in the field of computer vision, first proposed by (Viola and Wells III 1997; Maes et al. 1997) and has been employed as a robust method for comparing image similarity (Klein, Staring, and Pluim 2007; Kothandaraman et al. 2023) by binning pixel values into histograms and…”
Method
Metadata
- Authors
- Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P.
- Affiliations
- KU Leuven (BE)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1997-04
- Volume
- 16
- Issue
- 2
- Pages
- 187-198
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2034432063
- Cited by
- 4,547
- References
- 30
- Citations by year
- 2026: 80 · 2025: 84 · 2024: 93 · 2023: 85 · 2022: 116 · 2021: 130 · 2020: 121 · 2019: 124 · 2018: 171 · 2017: 153 · 2016: 139 · 2015: 183 · 2014: 195 · 2013: 212 · 2012: 209
- FWCI
- 71.00
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- multimodal image registration, mutual information, rigid registration, Kullback-Leibler divergence, partial overlap, image degradation
- MeSH terms
- Algorithms, Brain, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted
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