Hybrid GA Variational Bayes inference of finite mixture models for voxel classification in brain images

Li Sun, Yanning Zhang, Miao Ma, Guangjian Tian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper proposes a hybrid Genetic Algorithm (GA) and Variational Bayes (VB) inference Gaussian mixture model parameters estimation methodology for unsupervised voxel classification. Unlike Expectation-Maximization algorithm (EM), the mixture model distribution parameters are modeled by a set of hyper-parameters in VB inference framework. These hyper-parameters which characterize the mixture model distributions are estimated by a Variational Expectation-Maximization (VEM) learning algorithm. However, it is difficult to initialize the hyper-parameters for VEM algorithm without prior knowledge. This study introduces a hybrid GA and VEM methodology to estimate the finite mixture models fully automatically without any specific initialization steps for voxel classification in brain images. The proposed VEM, GAVEM algorithms are validated and compared with GA and EM algorithms on real three-dimensional human brain MRI images. The voxel tissue classification results demonstrate that VEM, and GAVEM algorithms achieve competitive segmentation results compared to other parameters estimation algorithms, and fully automatically.

Original languageEnglish
Title of host publicationSmart Materials and Intelligent Systems
Pages364-369
Number of pages6
DOIs
StatePublished - 2011
EventInternational Conference on Smart Materials and Intelligent Systems 2010, SMIS 2010 - Chongqing, China
Duration: 17 Dec 201020 Dec 2010

Publication series

NameAdvanced Materials Research
Volume143-144
ISSN (Print)1022-6680

Conference

ConferenceInternational Conference on Smart Materials and Intelligent Systems 2010, SMIS 2010
Country/TerritoryChina
CityChongqing
Period17/12/1020/12/10

Keywords

  • Bayes inference
  • Expectation-maximization algorithm (EM)
  • Finite mixture model (FMM)
  • Genetic algorithm (GA)
  • Image segmentation
  • Magnetic resonance imaging (MRI)

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