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Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration

  • Miaomiao Zhang
  • Nikhil Singh
  • P. Thomas Fletcher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

This paper presents a generative Bayesian model for diffeomorphic image registration and atlas building. We develop an atlas estimation procedure that simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations. To achieve this, we introduce a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms. An added benefit of this stochastic approach is that it can successfully solve difficult registration problems involving large deformations, where direct geodesic optimization fails. Using synthetic data generated from the forward model with known parameters, we demonstrate the ability of our model to successfully recover the atlas and regularization parameters. We also demonstrate the effectiveness of the proposed method in the atlas estimation problem for 3D brain images.

Keywords

Image Registration Image Atlas Computational Anatomy Hamiltonian Monte Carlo Image Registration Problem 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miaomiao Zhang
    • 1
  • Nikhil Singh
    • 1
  • P. Thomas Fletcher
    • 1
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

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