Advertisement

Adaptive Segmentation of MRI Data

  • W. M. WellsIII
  • W. E. L. Grimson
  • R. Kikinis
  • F. A. Jolesz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)

Abstract

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.

Keywords

Bias Field Intensity Inhomogeneity Tissue Class Tissue Classification Adaptive Segmentation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M Vannier, R. Butterfield, D. Jordan, W. Murphy, et al. Multi-Spectral Analysis of Magnetic Resonance Images. Radiology, (154): 221–224, 1985.Google Scholar
  2. 2.
    M. Kohn, N. Tanna, G. Herman, et al. Analysis of Brain and Cerebrospinal Fluid Volumes with MR Imaging. Radiology, (178): 115–122, 1991.Google Scholar
  3. 3.
    G. Gerig, W. Kuoni, R. Kikinis, and O. Kübler. Medical Imaging and Computer Vision: an Integrated Approach for Diagnosis and Planning. In Proc. 11’th DA GM Symposium, pages 425–443. Springer, 1989.Google Scholar
  4. 4.
    H.E. Cline, W.E. Lorensen, R. Kikinis, and F. Jolesz. Three-Dimensional Segmentation of MR Images of the Head Using Probability and Connectivity. JCAT, 14 (6): 1037–1045, 1990.Google Scholar
  5. 5.
    R.B. Lufkin, T. Sharpless, B. Flannigan, and W. Hanafee. Dynamic-Range Compression in Surface-Coil MRI. AJR, 147 (379): 379–382, 1986.CrossRefGoogle Scholar
  6. 6.
    L. Axel, J. Costantini, and J. Listerud. Intensity Correction in Surface-Coil MR Imaging. AJR, 148 (4): 418–420, 1987.CrossRefGoogle Scholar
  7. 7.
    K.O. Lim and A. Pfferbaum. Segmentation of MR Brain Images into Cerebrospinal Fluid Spaces, White and Gray Matter. JCAT, 13(4): 588–593, 1989.Google Scholar
  8. 8.
    B. Dawant, A. Zijdenbos, and R. Margolin. Correction of Intensity Variations in MR Images for Computer-Aided Tissue Classification. IEEE Trans. Med. Imaging, 12 (4): 770–781, 1993.CrossRefGoogle Scholar
  9. 9.
    J. Gohagan, E. Spitznagel, W. Murphy, M Vannier, et al. Multispectral Analysis of MR Images of the Breast. Radiology, (163): 703–707, 1987.Google Scholar
  10. 10.
    S. Aylward and J. Coggins. Spatially Invariant Classification of Tissues in MR Images. In Proceedings of the Third Conference on Visualization in Biomedical Computing. SPIE, 1994.Google Scholar
  11. 11.
    M. Kamber, D. Coffins, R. Shinghal, G. Francis, and A. Evans. Model-Based 3D Segmentation of Multiple Sclerosis Lesions in Dual-Echo MRI Data. In SPIE Vol. 1808, Visualization in Biomedical Computing 1992, 1992.Google Scholar
  12. 12.
    A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Roy. Statist. Soc., 39: 1–38, 1977.MathSciNetzbMATHGoogle Scholar
  13. 13.
    J.S. Lim. Two-Dimensional Signal and Image Processing. Prentice Hall, 1990.Google Scholar
  14. 14.
    B.R. Frieden. Probability, Statistical Optics, and Data Testing. Springer-Verlag, 1983.Google Scholar
  15. 15.
    W. Wells, R. Kikinis, W. Grimson, and F. Jolesz. Statistical Intensity Correction and Segmentation of Magnetic Resonance Image Data. In Proceedings of the Third Conference on Visualization in Biomedical Computing. SPIE, 1994.Google Scholar
  16. 16.
    R. Kikinis, M. Shenton, F.A. Jolesz, G. Gerig, J. Martin, M. Anderson, D. Metcalf, C. Guttmann, R.W. McCarley, W. Lorensen, and H. Cline. Quantitative Analysis of Brain and Cerebrospinal Fluid Spaces with MR Imaging. JMRI, 2: 619–629, 1992.CrossRefGoogle Scholar
  17. 17.
    R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.Google Scholar
  18. 18.
    Ron Kikinis et al. in preparation.Google Scholar
  19. 19.
    General Electric Medical Systems, Milwaukee, WI.Google Scholar
  20. 20.
    G. Gerig, O. Kübler, and F. Jolesz. Nonlinear Anisotropic Filtering of MRI data. IEEE Trans. Med. Imaging, (11): 221–232, 1992.Google Scholar
  21. 21.
    Sun Microsystems Inc., Mountain View, CA.Google Scholar
  22. 22.
    G. Ettinger, W. Grimson, T. Lozano-Pérez, W. Wells, S. White, and R. Kikinis. Automatic Registration for Multiple Sclerosis Change Detection. In Proceedings of the IEEE Workshop on Biomedical Image Analysis,Seattle, WA., 1994. IEEE.Google Scholar
  23. 23.
    W.E.L. Grimson, T. Lozano-Pérez, W. Wells, et al. An Automatic Registration Method for Frameless Stereotaxy, Image Guided Surgery, and Enhanced Realigy Visualization. In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition,Seattle, WA., June 1994. IEEE.Google Scholar
  24. 24.
    D. Stark and Jr. W. Bradley, editors. Magnetic Resonance Imaging. Mosby Year Book, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • W. M. WellsIII
    • 1
    • 2
  • W. E. L. Grimson
    • 2
  • R. Kikinis
    • 1
  • F. A. Jolesz
    • 1
  1. 1.Department of RadiologyHarvard Medical School and Brigham and Women’s HospitalBostonUSA
  2. 2.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations