A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models.

Full Abstract

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.

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Author information

Author/s: Joshi, Dhiraj (D); Li, Jia (J); Wang, James Z (JZ);

Affiliation: Department of Computer Science and Engineering, The Pennsylvania State University, University Park 16802, USA. djoshi(-atsign-)cse.psu.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process), published in United States. (Language: eng)

Reference: 2006-Jul; vol 15 (issue 7) : pp 1871-86

Dates: Created 2006/07/11; Completed 2006/08/08; Revised 2006/11/15;

PMID: 16830909, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Computer Graphics
Computer Simulation
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods

Information Storage and Retrieval - methods
Markov Chains
Models, Statistical
Numerical Analysis, Computer-Assisted
Signal Processing, Computer-Assisted
Stochastic Processes

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