An Empirical Model of Brain Shape

  • J. C. Gee
  • L. Le Briquer
Part of the Fundamental Theories of Physics book series (FTPH, volume 98)


A method is presented to systematically encode brain shape variation, observed from actual image samples, in the form of empirical distributions that can be applied to guide the Bayesian analysis of future image studies. Unlike eigendecompositions based on intrinsic features of a physical model, our modal basis for describing anatomic variation is derived directly from spatial mappings which bring previous brain samples into alignment with a reference configuration. The resultant representation ensures parsimony, yet captures information about the variation across the entire volumetric extent of the brain samples, and facilitates analyses that are governed by the measured statistics of anatomic variability rather than by the physics of some assumed model.

Key words

Bayesian image analysis Shape models Cerebral anatomy 


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  1. 1.
    J. C. Gee and R. K. Bajcsy, “Elastic matching: Continuum mechanical and probabilistic analysis,” in Brain Warping, A. Toga, ed., Academic Press, San Diego, In preparation.Google Scholar
  2. 2.
    J. C. Gee, D. R. Haynor, M. Reivich, and R. Bajcsy, “Finite element approach to warping of brain images,” in Medical Imaging 1994: Image Processing, M. H. Loew, ed., SPIE, Bellingham, 1994.Google Scholar
  3. 3.
    T. F. Cootes, D. H. Cooper, C. J. Taylor, and J. Graham, “Trainable method of parametric shape description,” Image and Vision Computing, 10, (5), pp. 289–294, 1992.CrossRefGoogle Scholar
  4. 4.
    A. Hill, T. F. Cootes, and C. J. Taylor, “A generic system for image interpretation using flexible templates,” in Proc. British Machine Vision Conference, pp. 276–285, 1992.Google Scholar
  5. 5.
    J. Martin, A. Pentland, S. Sclaroff, and R. Kikinis, “Characterization of neuropathological shape deformations,” IEEE Trans. Pattern Anal. Machine Intell, To appear.Google Scholar
  6. 6.
    L. Le Briquer and J. C. Gee, “Design of a statistical model of brain shape,” in Information Processing in Medical Imaging, J. S. Duncan and G. Gindi, eds., pp. 477–482, Springer-Verlag, Heidelberg, 1997.CrossRefGoogle Scholar
  7. 7.
    J. C. Gee, “Atlas warping for brain morphometry,” in Medical Imaging 1998: Image Processing, K. M. Hanson, ed., SPIE, Bellingham, To appear, 1998.Google Scholar
  8. 8.
    D. Heeger and J. Bergen, “Pyramid-based texture analysis/synthesis,” in ACM SIG-GRAPH, 1995.Google Scholar
  9. 9.
    S. C. Zhu, Y. Wu, and D. Mumford, “Filters, random fields, and maximum entropy (Frame) — Towards a unified theory for texture modeling,” Int. J. Comput. Vision, To appear.Google Scholar
  10. 10.
    K. Popat and R. W. Picard, “Cluster-based probability model and its application to image and texture processing,” IEEE Trans. Image Process., 6, (2), pp. 268–284, 1997.CrossRefGoogle Scholar
  11. 11.
    J. DeBonet and P. Viola, “A non-parametric multi-scale statistical model for natural images,” in Adv. in Neural Info. Processing, To appear, 1997.Google Scholar
  12. 12.
    E. P. Simoncelli, “Statistical models for images: compression, restoration and synthesis,” in 31st Asilomar Conf. Signals, Systems, and Computers, IEEE, New York, 1997.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • J. C. Gee
    • 1
  • L. Le Briquer
    • 2
  1. 1.Department of Neurology and GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA

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