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An Empirical Model of Brain Shape

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

Abstract

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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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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