Advertisement

Reconstruction of the central layer of the human cerebral cortex from MR images

  • Chenyang Xu
  • Dzung L. Pham
  • Jerry L. Prince
  • Maryam E. Etemad
  • Daphne N. Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.

Keywords

Central Layer Active Contour Model Intensity Inhomogeneity Human Cerebral Cortex Medial Frontal Gyrus 
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.

References

  1. 1.
    A. M. Dale and M. I. Sereno. Improved localization of cortical activity combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. J. Cogn. Neuroscience, 5(2): 162–176, 1993.CrossRefGoogle Scholar
  2. 2.
    D. MacDonald, D. Avis, and A. C. Evans. Multiple surface identification and matching in magnetic resonance images. In SPIE Proc. VBC’94, volume 2359, pages 160–169, 1994.CrossRefGoogle Scholar
  3. 3.
    S. Sandor and R. Leahy. Towards automated labelling of the cerebral cortex using a deformable atlas. In Information Processing in Medical Imaging, pages 127–138, 1995.Google Scholar
  4. 4.
    C. Davatzikos and R. N. Bryan. Using a deformable surface model to obtain a shape representation of the cortex. IEEE Trans. Med. Imag., 15:785–795, December 1996.CrossRefGoogle Scholar
  5. 5.
    H.A. Drury, D.C. Van Essen, C.H. Anderson, C.W. Lee, T.A. Coogan, and J.W. Lewis. Computerized mappings of the cerebral cortex: A multiresolution flattening method and a surfacebased coordinate system. J. Cogn. Neuroscience, pages 1–28, 1996.Google Scholar
  6. 6.
    C. Xu, D. L. Pham, and J. L. Prince. Finding the brain cortex using fuzzy segmentation, isosurfaces, and deformable surface models. In the XVth Int. Conf. Inf. Proc. Med. Imag. (IPMI), pages 399–404. Springer-Verlag, 1997.Google Scholar
  7. 7.
    M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321–331, 1987.CrossRefGoogle Scholar
  8. 8.
    L. D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. on Pattern Anal. Machine Intell., 15(11): 1131–1147, November 1993.CrossRefGoogle Scholar
  9. 9.
    D. L. Pham and J. L. Prince. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. In SPIE Medical Imaging ’98: Image Processing. SPIE, Feb. 21–27, 1998. to appear.Google Scholar
  10. 10.
    C. Xu and J. L. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. on Image Processing, pages 359–369, March 1998.Google Scholar
  11. 11.
    L. D. Cohen. On active contour models and balloons. CVGIP: Image Understanding, 53(2):211–218, March 1991.CrossRefGoogle Scholar
  12. 12.
    T. Kapur, E. Grimson, W. Wells, and R. Kikinis. Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis, 1(2): 109–127, 1996.CrossRefPubMedGoogle Scholar
  13. 13.
    N. W. Shock, R. C. Greulich, R. Andres, D. Arenberg, P. T. Costa Jr., E. Lakatta, and J. D. Tobin. Normal human aging: The Baltimore longitudinal study of aging. U.S. Governement Printing Office, Washington, D.C, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Chenyang Xu
    • 1
  • Dzung L. Pham
    • 1
    • 4
  • Jerry L. Prince
    • 1
    • 2
    • 3
  • Maryam E. Etemad
    • 2
  • Daphne N. Yu
    • 2
  1. 1.Electrical and Computer EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.Biomedical EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  3. 3.RadiologyThe Johns Hopkins UniversityBaltimoreUSA
  4. 4.Laboratory of Personality & CognitionGRC/NIA/NIHBaltimoreUSA

Personalised recommendations