MRI Tissue Segmentation Using a Variational Multilayer Approach

  • Ginmo Chung
  • Ivo D. Dinov
  • Arthur W. Toga
  • Luminita A. Vese
Conference paper

Abstract

We propose novel piecewise-constant minimization models for three-dimensional MRI brain data segmentation into white matter, gray matter, and cerebrospinal fluid. The proposed approaches are based on a multilayer or nested implicit surface evolution in variational form, well adapted to this problem. We propose two models, with and without using prior spatial information. The prior information is in the form of a probabilistic brain atlas, encoding spatial information of these three anatomical structures. Extensive experimental results and comparisons with manual segmentation and with automated segmentation are presented, together with quantitative assessment.

Keywords

MRI image segmentation Variational level set methods Active surfaces Partial differential equations Multilayer approach 

Notes

Acknowledgments

This work was funded by the NIH through the NIH Roadmap for Medical Research, Grant U54 RR021813 entitled Center for Computational Biology (CCB).

References

  1. 1.
    Chan, T.F., Vese, L.: An active contour model without edges. LNCS, 1682, 141–151 (1999)Google Scholar
  2. 2.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277 (2001)MATHCrossRefGoogle Scholar
  3. 3.
    Chung, G., Vese, L.A.: Energy minimization based segmentation and denoising using multilayer level set approach. LNCS, 3757, 439–455 (2005)Google Scholar
  4. 4.
    Chung, G., Vese, L.A.: Image segmentation using a multilayer level-set approach. Computing and Visualization in Science, 12(6), 267–285 (2009)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Chung, G., Dinov, I., Toga, A., Vese, L.A.: MRI tissue segmentation using a variational multilayer approach. UCLA C.A.M. Report 08-54 (2008)Google Scholar
  6. 6.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341–355 (2002)CrossRefGoogle Scholar
  7. 7.
    Han, X., Pham, D.L., Tosun, D., Rettmann, M.E., Xu, C., Prince, J.L.: CRUISE: cortical reconstruction using implicit surface evolution. NeuroImage, 23, 997–1012 (2004)CrossRefGoogle Scholar
  8. 8.
    Hornak, J.: The basics of MRI, a hypertext book on magnetic resonance imaging, 1997, and A web resource on NMR spectroscopy. North East Regional Meeting of the American Chemical Society. Postdam, NY (1999)Google Scholar
  9. 9.
    Joshi, M., Cui, J., Doolittle, K., Joshi, S., Van Essen, D., Wang, L., Miller, M.: Brain segmentation and the generation of cortical surfaces. NeuroImage, 9(5), 461–476 (1999)CrossRefGoogle Scholar
  10. 10.
    Kollokian, V.: Performance analysis of automatic techniques for tissue classification in magnetic resonance images of human brain. Master Thesis, Concordia University, CS Department, Montreal, Quebec, Canada (1996)Google Scholar
  11. 11.
    Liu, F., Gao, S., Gao, X.: Segmentation of MR images based on maximum a posterior. In: Proceedings of the 23rd Annual EMBS International Conference, Turkey (October 2001)Google Scholar
  12. 12.
    Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J.: A probabilistic atlas of the human brain: theory and rationale for its development. NeuroImage, 2, 89–101 (1995)CrossRefGoogle Scholar
  13. 13.
    Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. CPAM, 42, 577–685 (1989)MATHMathSciNetGoogle Scholar
  14. 14.
    Osher, S., Sethian, J.A.: Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. JCP, 79, 12–49 (1988)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Ratnanather, J.T., Priebe, C.E., Miller, M.I.: Semi-automated segmentation of cortical subvolumes via hierarchical mixture modeling. Proceedings of the SPIE Medical Imaging 2003: Image Processing, 5032, 1602–1612 (2003)Google Scholar
  16. 16.
    Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer, C.R.: Quo Vadis, Atlas-based segmentation? In: Jasjit S. Suri, David L. Wilson, and Swamy Laxminarayan Handbook of biomedical image analysis, vol. 3. Registration models. Springer, New York (2005)Google Scholar
  17. 17.
    Rousset, O.G., Ma, Y., Evans, A.C.: Correction for partial volume effects in pet: principle and validation. Journal of Nuclear Medicine, 39(5), 904–911 (1998)Google Scholar
  18. 18.
    Rousson, M., Deriche, R.: Adaptive segmentation of vector-valued images. In: Osher, S., Paragios, N. (eds.) Geometric level set methods. Springer, New York, pp. 195–205 (2003)CrossRefGoogle Scholar
  19. 19.
    Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97 (1998)CrossRefGoogle Scholar
  20. 20.
    Sowell, E.R., Thompson, P.M., Holmes, C.J., Batth, R., Jernigan, T.L., Toga, A.W.: Localizing age-related changes in brain structure between childhood and adolescence using statistical parametric mapping. NeuroImage, 9(6), 587–597 (1999)CrossRefGoogle Scholar
  21. 21.
    Talairach, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. Georg Thieme Verlag, New York (1988)Google Scholar
  22. 22.
    Tosun, D., Rettmann, M., Han, X., Tao, X., Xu, C., Resnick, S., Pham, D., Prince, J.: Cortical surface segmentation and mapping. NeuroImage, 23, S108–S118 (2004)Google Scholar
  23. 23.
    Tosun, D., Rettmann, M.E., Naiman, D.Q., Resnick, S.M., Kraut, M.A., Prince, J.L.: Cortical reconstruction using implicit surface evolution: accuracy and precision analysis. NeuroImage, 29(3), 838–852 (2006)CrossRefGoogle Scholar
  24. 24.
    Tsai, A., Yezzi, A., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing, 10(8), 1169–1186 (2001)MATHCrossRefGoogle Scholar
  25. 25.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 50(3), 271–293 (2002)MATHCrossRefGoogle Scholar
  26. 26.
    Woods, R., Grafton, S., Holmes, C., Cherry, S., Mazziotta, J.: Automated image registration: I. general methods and intrasubject, intramodality. Journal of Computer Assisted Tomography, 22(1), 139–152 (1998)CrossRefGoogle Scholar
  27. 27.
    Zaidi, H., Ruest, T., Schoenahl, F., Montandon, M.: Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET. NeuroImage, 32, 1591–1607 (2006)CrossRefGoogle Scholar
  28. 28.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Image Processing, 20(1), 45–57 (2001)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ginmo Chung
    • 1
  • Ivo D. Dinov
    • 2
  • Arthur W. Toga
    • 2
  • Luminita A. Vese
    • 1
  1. 1.Department of MathematicsUniversity of CaliforniaLos AngelesUSA
  2. 2.Laboratory of Neuro ImagingUniversity of CaliforniaLos AngelesUSA

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