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Hierarchical Bayesian Modeling, Estimation, and Sampling for Multigroup Shape Analysis

  • Yen-Yun Yu
  • P. Thomas Fletcher
  • Suyash P. Awate
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

This paper proposes a novel method for the analysis of anatomical shapes present in biomedical image data. Motivated by the natural organization of population data into multiple groups, this paper presents a novel hierarchical generative statistical model on shapes. The proposed method represents shapes using pointsets and defines a joint distribution on the population’s (i) shape variables and (ii) object-boundary data. The proposed method solves for optimal (i) point locations, (ii) correspondences, and (iii) model-parameter values as a single optimization problem. The optimization uses expectation maximization relying on a novel Markov-chain Monte-Carlo algorithm for sampling in Kendall shape space. Results on clinical brain images demonstrate advantages over the state of the art.

Keywords

Shape analysis hierarchical Bayes sampling in shape space 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yen-Yun Yu
    • 1
  • P. Thomas Fletcher
    • 1
  • Suyash P. Awate
    • 1
    • 2
  1. 1.Scientific Computing and Imaging (SCI) Institute, School of ComputingUniversity of UtahUtah
  2. 2.Computer Science and Engineering DepartmentIndian Institute of Technology (IIT)BombayIndia

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