Automatic quantification of multiple sclerosis lesion volume using stereotaxic space

  • Alex Zijdenbos
  • Alan Evans
  • Farhad Riahi
  • John Sled
  • Joe Chui
  • Vasken Kollokian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1131)


The quantitative analysis of MRI data is becoming increasingly important in the evaluation of therapies for the treatment of MS. This paper describes a processing environment for the automatic quantification of lesion load from large ensembles of MR volume data. The main components of this approach are stereotaxic transformation and multispectral classification, supported by pre- and postprocessing techniques to reduce noise and correct for intensity non-uniformities. The results of the automated approach are compared with those obtained by manual lesion delineation, showing a significant lesion volume correlation of 0.94.

Key words

MRI multiple sclerosis tissue quantification image segmentation stereotaxic space 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H. S. Choi, D. R. Haynor, and Y. Kim. Partial volume tissue classification of multichannel magnetic resonance images — a mixel model. IEEE Transactions on Medical Imaging, 10(3):395–407, Sept. 1991.CrossRefGoogle Scholar
  2. 2.
    A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal. Automated multi-modality image registration based on information theory. In Y. Bizais, C. Barillot, and R. D. Paola, editors, Information Processing in Medical Imaging (IPMI), pages 263–274. Kluwer, June 1995.Google Scholar
  3. 3.
    D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2):192–205, Mar./Apr. 1994.Google Scholar
  4. 4.
    A. Evans, M. Kamber, D. Collins, and D. MacDonald. An MRI-based probabilistic atlas of neuroanatomy. In S. D. Shorvon et al., editors, Magnetic Resonance Scanning and Epilepsy, chapter 48, pages 263–274. Plenum Press, 1994.Google Scholar
  5. 5.
    A. C. Evans, J. Frank, and D. H. Miller. Evaluation of multiple sclerosis lesion load: Comparison of image processing techniques: Summary of montreal workshop. Annals of Neurology, 1996. In press.Google Scholar
  6. 6.
    A. C. Evans, S. Marrett, P. Neelin, et al. Anatomical mapping of functional activation in stereotactic coordinate space. NeuroImage, 1:43–53, 1992.Google Scholar
  7. 7.
    G. Gerig, O. Kübler, R. Kikinis, and F. A. Jolesz. Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 11(2):221–232, June 1992.CrossRefGoogle Scholar
  8. 8.
    R. M. Henkelman and M. J. Bronskill. Artifacts in magnetic resonance imaging. Reviews of Magnetic Resonance in Medicine, 2(1):1–126, 1987.Google Scholar
  9. 9.
    M. Kamber, R. Shinghal, D. L. Collins, G. S. Francis, and A. C. Evans. Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Transactions in Medical Imaging, 14(3):442–453, Sept. 1995.Google Scholar
  10. 10.
    R. K.-S. Kwan, A. C. Evans, and G. B. Pike. An extensible MRI simulator for post-processing evaluation. In Proceedings of the Fourth International Conference on Visualization in Biomedical Computing (VBC), Hamburg, Germany, 1996.Google Scholar
  11. 11.
    J. R. Mitchell, S. J. Karlik, D. H. Lee, M. Eliasziw, G. P. Rice, and A. Fenster. Quantification of multiple sclerosis lesion volumes in 1.5 and 0.5T anisotropically filtered and unfiltered MR exams. Medical Physics, 23(1):115–126, Jan. 1996.Google Scholar
  12. 12.
    J. R. Mitchell, S. J. Karlik, D. H. Lee, and A. Fenster. Computer-assisted identification and quantification of multiple sclerosis lesions in MR imaging volumes in the brain. Journal of Magnetic Resonance Imaging, pages 197–208, Mar./Apr. 1994.Google Scholar
  13. 13.
    M. Özkan, B. M. Dawant, and R. J. Maciunas. Neural-network-based segmentation of multi-modal medical images: A comparative and prospective study. IEEE Transactions on Medical Imaging, 12(3):534–544, Sept. 1993.Google Scholar
  14. 14.
    D. W. Paty, D. K. B. Li, UBC MS/MRI Study Group, and IFNB Multiple Sclerosis Study Group. Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. Neurology, 43:662–667, 1993.Google Scholar
  15. 15.
    A. Simmons, P. S. Tofts, G. J. Barker, and S. R. Arridge. Sources of intensity nonuniformity in spin echo images. Magnetic Resonance in Medicine, 32:121–128, 1994.Google Scholar
  16. 16.
    J. Talairach and P. Tournoux. Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System — an Approach to Cerebral Imaging. Thieme Medical Publishers, New York, NY, 1988.Google Scholar
  17. 17.
    W. M. Wells III, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz. Statistical intensity correction and segmentation of MRI data. In Proceedings of the SPIE. Visualization in Biomedical Computing, volume 2359, pages 13–24, 1994.Google Scholar
  18. 18.
    D. A. G. Wicks, G. J. Barker, and P. S. Tofts. Correction of intensity nonuniformity in MR images of any orientation. Magnetic Resonance Imaging, 11(2):183–196, 1993.CrossRefGoogle Scholar
  19. 19.
    K. J. Worsley, A. C. Evans, S. Marrett, and P. Neelin. A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12(6):900–918, 1992.Google Scholar
  20. 20.
    K. J. Worsley, S. Marrett, P. Neelin, A. C. Vandal, K. J. Friston, and A. C. Evans. A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 1996. Accepted.Google Scholar
  21. 21.
    A. P. Zijdenbos, B. M. Dawant, and R. A. Margolin. Intensity correction and its effect on measurement variability in the computer-aided analysis of MRI. In Proceedings of the 9th International Symposium and Exhibition on Computer Assisted Radiology (CAR), pages 216–221, Berlin, Germany, June 1995.Google Scholar
  22. 22.
    A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer. Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE Transactions on Medical Imaging, 13(4):716–724, Dec. 1994.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Alex Zijdenbos
    • 1
  • Alan Evans
    • 1
  • Farhad Riahi
    • 1
  • John Sled
    • 1
  • Joe Chui
    • 1
  • Vasken Kollokian
    • 1
  1. 1.McConnell Brain Imaging Centre, Montréal Neurological InstituteMcGill UniversityMontréalCanada

Personalised recommendations