Comparison of Feature-Based Matching of CT and MR Brain Images

  • J. B. Antoine Maintz
  • Petra A. van den Elsen
  • Max A. Viergever
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)

Abstract

Geometrical image features like edges and ridges in digital images may be extracted by convolving the images with appropriate derivatives of Gaussians. The choice of the convolution operator and of the parameters of the Gaussian involved defines a specific feature image In this paper, various feature images derived from CT and MR brain images are defined and tested for usability and robustness in a correlation-based two and three dimensional matching algorithm. A number of these feature images is shown to furnish accurate matching results. The best results are obtained using gradient magnitude edgeness images.

Keywords

multi-modality registration/matching CT MRI differential geometry edge & ridge features scale space. 
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References

  1. 1.
    J. Blom. Topological and Geometrical Aspects of Image Structure. PhD thesis, Utrecht University, the Netherlands, 1992.Google Scholar
  2. 2.
    G. Borgefors. Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10: 849865, 1988.Google Scholar
  3. 3.
    George T. Y. Chen and Charles A. Pelizzari. Image Correlation Techniques in Radiation Therapy Planning. Computerized Medical Imaging and Graphics, 13 (3): 235–240, 1989.CrossRefGoogle Scholar
  4. 4.
    D. Eberly, R. Gardner, B. Morse, S. Pizer, and C. Scharlach. Ridges for image analysis. Technical report, dep. of Computer science, University of NC, Chapel Hill, Chapel Hill, NC, USA, 1993. submitted for publication.Google Scholar
  5. 5.
    L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever. Differential invariants in scale-space. Technical report 91–20, 1991.Google Scholar
  6. 6.
    L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever. On scale and the differential structure of images. Image 6 Vision Computing, 10: 376–388, 1992.CrossRefGoogle Scholar
  7. 7.
    A. Guéziec and N. Ayache. Smoothing and matching of 3-D space curves. In R.A. Robb, editor, Visualization in biomedical computing, volume 1808 of Proc. SPIE, pages 259–273. SPIE Press, Bellingham, WA, 1992.Google Scholar
  8. 8.
    P. F. Hemler, T. Sumanaweera, R. Pichumani, P. A. van den Eisen, S. Napel, and J. Adler. A system for multimodality image fusion. In IEEE symposium on computer-based medical systems,pages 335–340, Los Alamitos, CA, 1994. IEEE Computer Society Press.Google Scholar
  9. 9.
    L. Kitchen and A. Rosenfeld. Gray-level corner detection. Pattern Recognition Letters, 1: 95–102, 1982.CrossRefGoogle Scholar
  10. 10.
    J. J. Koenderink. The structure of images. Biological Cybernetics, (50): 363–370, 1984.Google Scholar
  11. 11.
    J. J. Koenderink and A. J. van Doom. Two-plus-one dimensional differential geometry. Pattern Recognition Letters, (15): 439–443, 1994.Google Scholar
  12. 12.
    D. N. Levin, C. A. Pelizzari, G. T. Y. Chen, C.T. Chen, and M. D. Cooper. Retrospective geometric correlation of MR, CT, and PET images. Radiology, 169(3): 817–823, 1988.Google Scholar
  13. 13.
    A. Liu, S. M. Pizer, D. Eberly, B. Morse, J. Rosenman, and V. Carrasco. Volume registration using the 3D core. In Proc. SPIE Medical Imaging VIII,Newport Beach, CA, February 1994. In press.Google Scholar
  14. 14.
    J. B. A. Maintz, P. A. van den Eisen, and M. A Viergever. Evaluation of ridge seeking operators for multimodality medical image matching. 1994. submitted for publication.Google Scholar
  15. 15.
    J. C. Maxwell. On hills and dales. The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science, 40 (269): 421–425, 1859.Google Scholar
  16. 16.
    O. Monga, S. Benayoun, and O. D. Faugeras. From partial derivatives of 3D density images to ridge lines. In R. A. Robb, editor, Visualization in biomedical computing, volume 1808 of Proc. SPIE, pages 118–127. SPIE Press, Bellingham, WA, 1992.Google Scholar
  17. 17.
    C. A. Pelizzari, G. T. Y. Chen, D. R. Spelbring, R. R. Weichselbaum, and C. T. Chen. Accurate three-dimensional registration of CT, PET, and/or MR images of the brain. Computer Assisted Tomography, 13(1): 20–26, 1989.Google Scholar
  18. 18.
    C. F. Ruff, D. L. G. Hill, G. P. Robinson, and D. J. Hawkes. Volume Rendering of Multimodal Images for the Planning of Skull Base Surgery. In H. U. Lemke et al, editor, Computer Assisted Radiology ‘88, pages 574–579. Springer-Verlag, 1993.Google Scholar
  19. 19.
    B. M. ter Haar Romeny, L. M. J. Florack, J. J. Koenderink, and M. A. Viergever. Scale-space: its natural operators and differential invariants. In Colchester and Hawkes, editors, Information Processing in Medical Imaging, Proceedings of the 12th International Conference, Springer-Verlag, Berlin, 1991.Google Scholar
  20. 20.
    J. Thirion. Extremal points: definition and application to 3D image registration. In Proc. CVPR,pages 587–592, Los alamitos, CA, 1994. IEEE Computer Society press.Google Scholar
  21. 21.
    P. A. van den Elsen. Multimodality matching of brain images. PhD thesis, Utrecht University, the Netherlands, 1993.Google Scholar
  22. 22.
    P. A. van den Elsen, J. B. A. Maintz, E. J. D. Pol, and M. A. Viergever. Image fusion using geometrical features. In R. A. Robb, editor, Visualization in biomedical computing, volume 1808 of Proc. SPIE, pages 172–186. SPIE Press, Bellingham, WA, 1992.Google Scholar
  23. 23.
    P. A. van den Elsen, J. B. A. Maintz, and M. A. Viergever. Automated CT and MR brain image matching using correlation of geometrical “ridgeness” features. Technical Report 3DCV 93–07, Utrecht University, 1993. submitted for publication.Google Scholar
  24. 24.
    P. A. van den Elsen, J. B. A. Maintz, and M. A. Viergever. Geometry driven multimodality matching of brain images. Brain Topography, 5: 153–158, 1993.CrossRefGoogle Scholar
  25. 25.
    P. A. van den Elsen, E. J. D. Pol, T. S. Sumanaweera, P.F. Hemler, S. Napel, and J. Adler. Grey value correlation techniques used for automatic matching of CT and MR brain and spine images. In Visualization in Biomedical Computing, volume 2359 of Proc. SPIE, pages 227–237. SPIE Press, Bellingham, WA, 1994.Google Scholar
  26. 26.
    P. A. van den Elsen, E.D. Pol, and M. A. Viergever. Medical image matching-a review with classification. IEEE Engineering in medicine and biology, 12 (1): 26–39, 1993.CrossRefGoogle Scholar
  27. 27.
    P. A. van den Elsen and M. A. Viergever. Marker guided multimodality matching of the brain. European Radiology, 4: 45–51, 1994.Google Scholar
  28. 28.
    A. Witkin. Scale space filtering. In Proceedings of the 7th International Confernece on Artificial Intelligence, pages 1019–1021, Karlsruhe, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • J. B. Antoine Maintz
    • 1
  • Petra A. van den Elsen
    • 1
    • 2
  • Max A. Viergever
    • 1
  1. 1.Computer Vision Research GroupUniversity Hospital UtrechtUtrechtThe Netherlands
  2. 2.Radiological Sciences LaboratoryStanford University School of MedicineStanfordUSA

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