Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate

  • Christian Brechbühler
  • Guido Gerig
  • Gábor Székely
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1131)


A novel bias correction technique is proposed based on the estimation of the parameters of a polynomial bias field directly from image data. The procedure overcomes difficulties known from homomorphic filtering or from techniques assuming an initial presegmented image. The only parameters are a set of expected class means and the standard deviation. Applications to various MR images illustrate the performance.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M.W. Vannier, Speidel Ch.M., D.L. Rickman, L.D. Schertz, et al. Validation of Magnetic Resonance Imaging (MRI) Multispectral Tissue Classification. In Proc. of 9th Int. Conf. on Pattern Recognition, ICPR'88, pages 1182–1186, November 1988.Google Scholar
  2. 2.
    K.O. Lim and A.J. Pfefferbaum. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. Journal of Computer Assisted Tomography, 13:588–593, 1989.Google Scholar
  3. 3.
    H.E. Cline, W.E. Lorensen, St.P. Souza, F.A. Jolesz, R. Kikinis, G. Gerig, and Th.E. Kennedy. 3D surface rendered MR images of the brain and its vasculature. Journal of Computer Assisted Tomography, 15(2):344–351, March 1991.Google Scholar
  4. 4.
    M.I. Kohn, N.K. Tanna, G.T. Herman, S.M. Resnick, P.D. Mozley, R.E. Gur, A. Alavi, R.A. Zimmerman, and R.C. Gur. Analysis of brain and cerebrospinal fluid volumes with MR imaging. Part I. Methods, reliability, and validation. Radiology, 178:115–122, January 1991.Google Scholar
  5. 5.
    B.M. Dawant, A.P. Zjidenbos, and R.A. Margolin. Correction of intensity variations in MR images for compuer-aided tissue classification. IEEE Transactions on Medical Imaging, 12(4):770–781, 1993.CrossRefGoogle Scholar
  6. 6.
    Charles R. Meyer, Peyton H. Bland, and James Pipe. Retrospective correction of Intensity Inhomogeneities in MRI. IEEE Transactions on Medical Imaging, 14(1):36–41, March 1995.Google Scholar
  7. 7.
    William Wells, Ron Kikinis, and Ferenc A. Jolesz. Statistical intensity correction and segmentation of magnetic resonance image data. In Richard A. Robb, editor, Proceedings of the Third Conference on Visualization in Biomedical Computing VBC'94, volume 2359, pages 13–24. SPIE, October 1994.Google Scholar
  8. 8.
    Colin R. Reeves. Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publications, 1993.Google Scholar

Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  • Christian Brechbühler
    • 1
  • Guido Gerig
    • 1
  • Gábor Székely
    • 1
  1. 1.Communication Technology LaboratoryETH-ZentrumZurichSwitzerland

Personalised recommendations