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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 10 A 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.

journal_name

J Struct Biol

journal_title

Journal of structural biology

authors

Singh V,Marinescu DC,Baker TS

doi

10.1016/j.jsb.2003.11.028

keywords:

subject

Has Abstract

pub_date

2004-01-01 00:00:00

pages

123-41

issue

1-2

eissn

1047-8477

issn

1095-8657

journal_volume

145

pub_type

杂志文章

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