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PMID: 15065681 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.

Journal of structural biology ·Vol. 145 ·No. 1-2 ·2004-00-00 ·Pages 142-51

Adiga PS, Malladi R, Baxter W, Glaeser RM

Abstract

Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a computer screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.

MeSH Terms
Algorithms Anisotropy Cryoelectron Microscopy/methods Electronic Data Processing/methods Image Enhancement Image Processing, Computer-Assisted/methods Particle Size Pattern Recognition, Automated Ribosomes/chemistry,ultrastructure Software Design
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Adiga P S Umesh
Physical Biosciences Division, LBNL, 1, Cyclotron Road, Berkeley, CA 94720, USA. upadiga@lbl.gov
Malladi Ravi
Baxter William
Glaeser Robert M
Article Info
Journal
Journal of structural biology
Abbr.
J Struct Biol
ISSN
1047-8477
Published
2004-00-00
Pages
142-51
Language
English
Region
United States
NLM ID
9011206
Subset
IM
Grants
NIGMS NIH HHS · GM62989 · United States
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