A Bayesian approach for construction of sparse statistical shape models using Dirichlet distribution

Ali Gooya, Elaheh Mousavi, Christos Davatzikos, Hongen Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Statistical shape models (SSMs) made using point sets are important tools to capture the variations within shape populations. One popular method for construction of SSMs is based on the Expectation-Maximization (EM) algorithm which establishes probabilistic matches between the model and training points. In this paper, we propose a novel Bayesian framework to automatically determine the optimal number of the model points. We use a Dirichlet distribution as a prior to enforce sparsity on the mixture weights of Gaussians. Insignificant model points are determined and pruned out using a quadratic programming technique. We apply our method to learn a sparse SSM from 15 manually segmented caudate nuclei data sets. The generalization ability of the proposed model compares favorably to a traditional EM based model.

Original languageEnglish (US)
Title of host publicationAugmented Reality Environments for Medical Imaging and Computer-Assisted Interventions - 6th Int. Workshop, MIAR 2013 and 8th Int. Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013, Proc.
Pages144-152
Number of pages9
DOIs
StatePublished - Dec 30 2013
Event6th International Workshop on Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8090 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/22/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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