3D Multi-Modality Medical Image Registration Using Feature Space Clustering

  • André Collignon
  • Dirk Vandermeulen
  • Paul Suetens
  • Guy Marchal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)


In this paper, 3D voxel-similarity-based (VB) registration algorithms that optimize a feature-space clustering measure are proposed to combine the segmentation and registration process. We present a unifying definition and a classification scheme for existing VB matching criteria and propose a new matching criterion: the entropy of the grey-level scatter-plot. This criterion requires no segmentation or feature extraction and no a priori knowledge of photometric model parameters. The effects of practical implementation issues concerning grey-level resampling, scatter-plot binning, parzen-windowing and resampling frequencies are discussed in detail and evaluated using real world data (CT and MRI).


Registration Algorithm Match Criterion Image Grid Neighbour Interpolation Trilinear Interpolation 
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. 1.
    Brown L.G.: A Survey of Image Registration Techniques. ACM Computing Surveys 24: 4 (1992) 325–376CrossRefGoogle Scholar
  2. 2.
    Collignon A., Vandermeulen D., Suetens P., Marchal G.: Surface based registration of 3D medical images. SPIE Int’l Conf. Medical Imaging 1993: Image Processing, 14–19 februari, 1993, Newport Beach, California, USA. SPIE 1898 (1993) 32–42CrossRefGoogle Scholar
  3. 3.
    Collignon A., Vandermeulen D., Suetens P., Marchal G.: An Object Oriented Tool for 3D Multimodality Surface-based Image Registration. Computer Assisted Radiology, CAR93, 24–26 juni, 1993, Berlin, 568–573Google Scholar
  4. 4.
    Collignon A., Vandermeulen D., Suetens P., Marchai G.: Registration of 3D Multi-Modality Medical Images Using Surfaces and Point Landmarks. Pattern Recognition Letters 15 (1994) 461–467CrossRefzbMATHGoogle Scholar
  5. 5.
    Collignon A., Vandermeulen D., Suetens P., Marchai G.: Automatic Registration of 3D Images of the Brain Based on Fuzzy Objects. SPIE Int’l Conf. Medical Imaging 1994: Image Processing, 13–18 februari, 1994, Newport Beach, California, USA. SPIE 2167 (1994) 162–175CrossRefGoogle Scholar
  6. 6.
    COVIRA, Computer Vision in Radiology: Deliverable 55, D5/2.5 - Demonstration of Final Pilot System for Conformal/Stereotactic Radiotherapy Planning. AIM Programme Project A2003 of The European Commission DG X III, April 1994Google Scholar
  7. 7.
    Duda R.O., Hart P.E.: Pattern Classification and Scene Analysis. Stanford Research Institute, Menlo Park, CA, USA, A Wiley-Interscience Publication (1973)Google Scholar
  8. 8.
    Gerlot-Chiron P., Bizais Y.: Registration of Multimodality Medical Images Using Region Overlap Criterion. CVGIP: Graphical Models and Image Processing 54: 5 (1992) 396–406CrossRefGoogle Scholar
  9. 9.
    Hill D.L.G., Studholme C., Hawkes D.J.: Voxel Similarity Measures for Automated Image Registration. SPIE Int’l Conf. on Visualization in Biomedical Computing, October 4–7, 1994. SPIE 2359 (1994) in pressGoogle Scholar
  10. 10.
    Mangin J.-F., Frouin V., Bloch I., Bendriem B., J. Lopez-Krahe: Fast nonsupervised 3D registration of PET and MR images of the brain. Journal of Cerebral Blood Flow and Metabolism 14 (1994) 749–762CrossRefGoogle Scholar
  11. 11.
    Maurer C.R., Fitzpatrick J.M.: A Review of Medical Image Registration. Interactive Image-Guided Neurosurgery, Maciunas R.J. (Ed), Park Ridge, IL, American Association of Neurological Surgeons (1993) 17–44Google Scholar
  12. 12.
    Neiw H.M., Chen C-T., Lin W.C., Pelizzari C.A.: Automated three-dimensional registration of medical images. SPIE Int’1 Conf. Medical Imaging V: Image Processing, California, USA. SPIE 1445 (1991) 259–264CrossRefGoogle Scholar
  13. 13.
    Shanmugam K.S.: Digital and Analog Communication Systems, University of Kansas, John Wiley Sons (1979)Google Scholar
  14. 14.
    Van den Eisen P.A., Maintz J.B.A., Pol E.J.D., Viergever M.A.: Image fusion using geometrical features. SPIE Int’l Conf. on Visualization in Biomedical Computing, 1992. SPIE 1808 (1992) 172–186CrossRefGoogle Scholar
  15. 15.
    Van den Eisen P.A., Pol E-J.D., Viergever M.A.: Medical Image Matching–A Review with Classification. IEEE Engeneering in Medicine and Biology Magazine 12: 1 (1993) 26–38CrossRefGoogle Scholar
  16. 16.
    Van den Eisen P.A., Pol E.J.D., Sumanaweera T.S., Hemler P.F., Napel S., Adler J.R.: Grey value correlation techniques used for automatic matching of CT and MR brain and spine images. SPIE Int’l Conf. on Visualization in Biomedical Computing, October 4–7, 1994. SPIE 2359 (1994) 227–237Google Scholar
  17. 17.
    Van Herk M., Kooy H.M.: Automatic three-dimensional correlation of CT-CT, CT-MRI, and CT-SPECT using chamfer matching. Med. Phys. 21:7 (1994) 11631178Google Scholar
  18. 18.
    Verbeeck R., Vandermeulen D., Michiels J., Suetens P., Marchai G.: Computer Assisted Stereotactic Neurosurgery. Image and Vision Computing 11: 8 (1993) 468–485CrossRefGoogle Scholar
  19. 19.
    Woods R.P., Mazziotta J.C., Cherry S.R.: MRI-PET Registration with Automated Algorithm. Journal of Computer Assisted Tomography 17: 4 (1993) 536–546CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • André Collignon
    • 1
    • 2
    • 3
  • Dirk Vandermeulen
    • 1
    • 2
    • 3
  • Paul Suetens
    • 1
    • 2
    • 3
  • Guy Marchal
    • 1
    • 2
    • 3
  1. 1.Laboratory for Medical Imaging ResearchKatholieke UniversiteitLeuvenBelgium
  2. 2.Department of Electrical EngineeringESATHeverleeBelgium
  3. 3.Department of RadiologyUniversity Hospital GasthuisbergLeuvenBelgium

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