Title

Image Segmentation For Automatic Particle Identification In Electron Micrographs Based On Hidden Markov Random Field Models And Expectation Maximization

Abstract

Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10Å 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 á 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. © 2003 Elsevier Inc. All rights reserved.

Publication Date

1-1-2004

Publication Title

Journal of Structural Biology

Volume

145

Issue

1-2

Number of Pages

123-141

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jsb.2003.11.028

Socpus ID

0347985832 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0347985832

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