Abstract
Over the last five years, new “voxel-based” approaches have allowed important progress in multimodal image registration, notably due to the increasing use of information-theoretic similarity measures. Their wide success has led to the progressive abandon of measures using standard image statistics (mean and variance). Until now, such measures have essentially been based on heuristics. In this paper, we address the determination of a new measure based on standard statistics from a theoretical point of view. We show that it naturally leads to a known concept of probability theory, the correlation ratio. In our derivation, we take as the hypothesis the functional dependence between the image intensities. Although such a hypothesis is not as general as possible, it enables us to model the image smoothness prior very easily. We also demonstrate results of multimodal rigid registration involving Magnetic Resonance (MR), Computed Tomography (CT), and Positron Emission Tomography (PET) images. These results suggest that the correlation ratio provides a good trade-off between accuracy and robustness.
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Keywords
- Positron Emission Tomography
- Mutual Information
- Positron Emission Tomography Image
- Image Registration
- Functional Dependence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
R. E. Blahut. Principles and Practice of Information Theory. Addison-Wesley Pub. Comp., 1987.
M. Bro-Nielsen. Rigid Registration of CT, MR and Cryosection Images Using a GLCM Framework. CVRMed-MRCAS’97, pages 171–180, March 1997.
L. G. Brown. A survey of image registration techniques. ACM Computing Surveys, 24(4):325–376, 1992.
D. L. G. Hill and D. J. Hawkes. Medical image registration using voxel similarity measures. AAAI Sping Symposium Series: Applications of Comp. Vision in Med. Im. Proces., pages 34–37, 1994.
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens. Multimodality Image Registration by Maximization of Mutual Information. IEEE Transactions on Medical Imaging, 16(2):187–198, 1997.
J. B. A. Maintz and M. A. Viergever. A survey of medical image registration. MedIA, 2(1):1–36, 1998.
G. Malandain, S. Fernández-Vidal, and J.C. Rocchisani. Improving registration of 3-D images using a mechanical based method. EGCV’94, pages 131–136, May 1994.
C. R. Meyer, J. L. Boes, B. Kim, P. H. Bland, K. R. Zasadny, P. V. Kison, K. Koral, K. A. Frey, and R. L. Wahl. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate warped geometric deformations. Medical Image Analysis, 1(3):195–206, 1996/7.
C. Nikou, F. Heitz, J.-P. Armspach, and I.-J. Namer. Single and multimodal subvoxel registration of dissimilar medical images using robust similarity measures. SPIE Conference on Medical Imaging, February 1998.
A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Inc., third edition, 1991.
A. Roche, G. Malandain, X. Pennec, and N. Ayache. Multimodal Image Registration by Maximization of the Correlation Ratio. Technical Report 3378, INRIA, March 1998.
G. Saporta. Probabilités, analyse des données et statistique. Editions Technip, Paris, 1990.
C. Studholme, D. L. G. Hill, and D. J. Hawkes. Automated 3-D registration of MR and CT images of the head. Medical Image Analysis, 1(2):163–175, 1996.
P. A. van den Elsen, E.-J. D. Pol, T. S. Sumanaweera, P. F. Hemler, S. Napel, and J. R. Adler. Grey value correlation techniques for automatic matching of CT and MR brain and spine images. Proc. Visualization in Biomed. Comp., 2359:227–237,October 1994.
P.A. van den Elsen, E.J.D. Pol, and M.A. Viergever. Medical image matching — a review with classification. IEEE Engineering in Medicine and Biology, 12(4):26–39, march 1993.
P. Viola. Alignment by Maximization of Mutual Information. PhD thesis, M.I.T. Artificial Intelligence Laboratory, 1995. also A.I.T.R. No. 1548, available at ftp://publications.ai.mit.edu.
P. Viola and W. M. Wells. Alignment by Maximization of Mutual Information. Intern. J. of Gomp. Vision, 24(2):137–154, 1997.
W. M. Wells, P. Viola, H. Atsumi, and S. Nakajima. Multi-modal volume registration by maximization of mutual information. Medical Image Analysis, 1(1):35–51, 1996.
J. West and al. Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Gomp. Assist. Tomography, 21:554–566, 1997.
R. P. Woods, S. R. Cherry, and J. C. Mazziotta. Rapid Automated Algorithm for Aligning and Reslicing PET Images. Journal of Gomp. Assist. Tomography, 16(4):620–633, 1992.
R. P. Woods, J. C. Mazziotta, and S. R. Cherry. MRI-PET Registration with Automated Algorithm. Journal of Comp. Assist. Tomography, 17(4):536–546, 1993.
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© 1998 Springer-Verlag Berlin Heidelberg
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Roche, A., Malandain, G., Pennec, X., Ayache, N. (1998). The correlation ratio as a new similarity measure for multimodal image registration. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056301
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DOI: https://doi.org/10.1007/BFb0056301
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