Title

Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

Authors

Authors

V. Singh; D. C. Marinescu;T. S. Baker

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Struct. Biol.

Keywords

EDGE-DETECTION; RECONSTRUCTION; CRYOMICROSCOPY; Biochemistry & Molecular Biology; Biophysics; Cell Biology

Abstract

Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 Angstrom requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields-a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum A posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required. (C) 2003 Elsevier Inc. All rights reserved.

Journal Title

Journal of Structural Biology

Volume

145

Issue/Number

1-2

Publication Date

1-1-2004

Document Type

Article; Proceedings Paper

Language

English

First Page

123

Last Page

141

WOS Identifier

WOS:000188192500014

ISSN

1047-8477

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