Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation

Jun Zhang, James W. Modestino, David A. Langan

Research output: Contribution to journalArticle

144 Scopus citations

Abstract

An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte-Carlo scheme and a scheme related to Besag's iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided.

Original languageEnglish (US)
Pages (from-to)404-420
Number of pages17
JournalIEEE Transactions on Image Processing
Volume3
Issue number4
DOIs
StatePublished - Jul 1994

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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