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Bayesian Principal Geodesic Analysis in Diffeomorphic Image Registration

  • Miaomiao Zhang
  • P. Thomas Fletcher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Computing a concise representation of the anatomical variability found in large sets of images is an important first step in many statistical shape analyses. In this paper, we present a generative Bayesian approach for automatic dimensionality reduction of shape variability represented through diffeomorphic mappings. To achieve this, we develop a latent variable model for principal geodesic analysis (PGA) that provides a probabilistic framework for factor analysis on diffeomorphisms. Our key contribution is a Bayesian inference procedure for model parameter estimation and simultaneous detection of the effective dimensionality of the latent space. We evaluate our proposed model for atlas and principal geodesic estimation on the OASIS brain database of magnetic resonance images. We show that the automatically selected latent dimensions from our model are able to reconstruct unseen brain images with lower error than equivalent linear principal components analysis (LPCA) models in the image space, and it also outperforms tangent space PCA (TPCA) models in the diffeomorphism setting.

Keywords

Image Registration Latent Variable Model Average Mean Square Error Geodesic Path Shape Variability 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Miaomiao Zhang
    • 1
  • P. Thomas Fletcher
    • 1
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA

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