Multimodal Image Registration for Efficient Multi-resolution Visualization

  • Joerg Meyer
Part of the Mathematics and Visualization book series (MATHVISUAL)


Arising from the clinical need for multimodal imaging, an integrated system for automated multimodal image registration and multi-source volume rendering has been developed, enabling simultaneous processing and rendering of image data from structural and functional medical imaging sources. The algorithms satisfy real-time data processing constraints, as required for clinal deployment.

The system represents an integrated pipeline for multimodal diagnostics comprising of multiple-source image acquisition; efficient, wavelet-based data storage; automated image registration based on mutual information and histogram transformations; and texture-based volume rendering for interactive rendering on multiple scales.

Efficient storage and processing of multimodal images as well as histogram transformation and registration will be discussed. It will be shown how the conflict of variable resolutions that occurs when using different modalities can be resolved efficiently by using a wavelet-based storage pattern, which also offers advantages for multi-resolution rendering.


Mutual Information Image Registration Linear Transfer Function Histogram Match Medical Image Registration 
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.


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  1. [Ash95]
    Ashley J., Barber R., Flickner M., Lee D., Niblack W., and Petkovic D.: Automatic and semiautomatic methods for image annotation and retrieval in qbic. In: Proc. SPIE Storage and Retrieval for Image and Video Databases III, 24-35 (1995)Google Scholar
  2. [Bro92]
    Brown L. G.: A survey of image registration techniques. ACM Computing Surveys 24, 4, 325–376 (1992)CrossRefGoogle Scholar
  3. [But01]
    Butz T. and Thiran J.: Affine registration with feature space mututal information. In: Lecture Notes in Computer Science 2208: MICCAI 2001, Springer-Verlag Berlin Heidelberg, 549–556 (2001)Google Scholar
  4. [Cri00]
    Cristiani N. and Shaw-Taylor J.: Suport Vector Machines and other kernel-based learning methods. Cambridge U. Press (2000)Google Scholar
  5. [Els94]
    Van den Elsen P.A., Pol D. E., Sumanaweera S.T., Hemler P. F., Napel S., Adler J. R.: Grey value correlation techniques used for automatic matching of CT and MR brain and spine images. Proc. Visualization in Biomedical Computing, SPIE 2359, 227-237 (1994)Google Scholar
  6. [Els95]
    Van den Elsen P. A., Maintz J. B. A., Pol D. E., Viergever M. A.: Automatic registration of CT and MR brain images using correlation of geo-metrical features. IEEE Transactions on Medical Imaging 14, 2, 384–396 (1995)CrossRefGoogle Scholar
  7. [Gon02]
    Gonzalez R. C., Woods R. E.: Digital Image Processing. Prentice Hall (2002)Google Scholar
  8. [Hil01]
    Hill D., Batchelor P., Holden M., and Hawkes D.: Medical image registration. Phys. Med. Biol., 26, R1–R45 (2001)CrossRefGoogle Scholar
  9. [Hua98]
    Huang J., Kumar S., Mitra M., and Zhu W.: Spatial color indexing and applications. In: Proc. of IEEE International Conf. Computer Vision ICCV ’98, Bombay, India, 602-608 (1998)Google Scholar
  10. [Jac77]
    Jacobs D. A. H.: The state of the art in numerical analysis. Academic Press, London (1977)Google Scholar
  11. [Jen02]
    Jenkinson M., Bannister P., Brady M., and Smith S.: Improved methods for the registration and motion correction of brain images. Technical report, Oxford University (2002)Google Scholar
  12. [Lef01]
    Lefébure M. and Cohen L.: Image registration, optical flow and local rigidity. J. Mathematical Imaging and Vision, 2(14), 131–147 (2001)CrossRefGoogle Scholar
  13. [LoC03]
    Lo C. H., Guo Y., Lu C. C.: A binarization approach to CT-MR registration using Normalized Mutual Information. Proc. IASTED Signal and Image Processing, 399 (2003)Google Scholar
  14. [Mae97]
    Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16, 2, 187–198 (1997)CrossRefGoogle Scholar
  15. [Mai98]
    Maintz J. B. and Viergever M.: A survey of medical image registration. Medical Image Analysis, 1(2), 1–36 (1998)CrossRefGoogle Scholar
  16. [Mey97]
    Meyer C. R., Boes J. L., Kim B., Bland P. H., Zasadny K. R., Kison P. V., Koral K. F., Frey K. A., and Wahl R. L.: Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Medical Image Analysis, 2(1), 195–206 (1997)CrossRefGoogle Scholar
  17. [Mey03]
    Meyer J., Borg R., Takanashi I., Lum E. B., and Hamann B.:Segmentation and Texture-based Hierarchical Rendering Techniques for Large-scale Real-color Biomedical Image Data. In: Post F. H., Nielson G. H., Bonneau G.-P., eds., Data Visualization - The State of the Art, Kluwer Academic Publishers, Boston, 169–182 (2003)Google Scholar
  18. [Pen98]
    Penney C., Weese J., Little J., Hill, D., and Hawkes, D.: A comparison of similarity measures for used in 2-D-3-D medical image registration. IEEE Trans. on Medical Imaging, 4(17), 586–595 (1998)CrossRefGoogle Scholar
  19. [Plu03]
    Pluim J. P. W., Maintz J. B. A., Viergever M. A.: Mutual Information based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22, 8, 896–1004 (2003)CrossRefGoogle Scholar
  20. [Sto98]
    Stoica R., Zerubia J., and Francos J. M.: The two-dimensional wold decomposition for segmentation and indexing in image libraries. In: Proc. IEEE Int. Conf. Acoust., Speech, and Sig. Proc., Seattle (1998)Google Scholar
  21. [Stu96]
    Studholme C., Hill D. L. G., Hawkes D. J.: Automated 3-D registration of MR and CT images of the head. Medical Image Analysis 1, 2, 163–175 (1996)CrossRefGoogle Scholar
  22. [Vio95]
    Viola P. and Wells III W. M.: Alignment by maximization of mutual information. In: Proceedings of IEEE International Conference on Computer Vision, Los Alamitos, CA, 16-23 (1995)Google Scholar
  23. [Wel96]
    Wells W. M., Viola P., Atsumi H., Nakajima S., Kikinis R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1, 1, 35–51 (1996)CrossRefGoogle Scholar
  24. [Zhu02]
    Zhu Y. M.: Volume image registration by cross-entropy optimization. IEEE Transactions on Medical Imaging 21, 174–180 (2002)CrossRefGoogle Scholar

Copyright information

© Springer 2008

Authors and Affiliations

  • Joerg Meyer
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceIrvine

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