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CONTRIBUTIONS TO AUTOMATIC PARTICLE IDENTIFICATION IN ELECTRON MICROGRAPHS: ALGORITHMS, IMPLEMENTATION, AND APPLICATIONS

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Date Issued:
2005
Abstract/Description:
Three dimensional reconstruction of large macromolecules like viruses at resolutions below 8 \AA~ - 10 \AA~ requires a large set of projection images and the particle identification step becomes a bottleneck. Several automatic and semi-automatic particle detection algorithms have been developed along the years. We present a general technique designed to automatically identify the projection images of particles. The method utilizes Markov random field modelling of the projected images and involves a preprocessing of electron micrographs followed by image segmentation and post processing for boxing of the particle projections. Due to the typically extensive computational requirements for extracting hundreds of thousands of particle projections, parallel processing becomes essential. We present parallel algorithms and load balancing schemes for our algorithms. The lack of a standard benchmark for relative performance analysis of particle identification algorithms has prompted us to develop a benchmark suite. Further, we present a collection of metrics for the relative performance analysis of particle identification algorithms on the micrograph images in the suite, and discuss the design of the benchmark suite.
Title: CONTRIBUTIONS TO AUTOMATIC PARTICLE IDENTIFICATION IN ELECTRON MICROGRAPHS: ALGORITHMS, IMPLEMENTATION, AND APPLICATIONS.
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Name(s): Singh, Vivek, Author
Marinescu, Dan, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2005
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Three dimensional reconstruction of large macromolecules like viruses at resolutions below 8 \AA~ - 10 \AA~ requires a large set of projection images and the particle identification step becomes a bottleneck. Several automatic and semi-automatic particle detection algorithms have been developed along the years. We present a general technique designed to automatically identify the projection images of particles. The method utilizes Markov random field modelling of the projected images and involves a preprocessing of electron micrographs followed by image segmentation and post processing for boxing of the particle projections. Due to the typically extensive computational requirements for extracting hundreds of thousands of particle projections, parallel processing becomes essential. We present parallel algorithms and load balancing schemes for our algorithms. The lack of a standard benchmark for relative performance analysis of particle identification algorithms has prompted us to develop a benchmark suite. Further, we present a collection of metrics for the relative performance analysis of particle identification algorithms on the micrograph images in the suite, and discuss the design of the benchmark suite.
Identifier: CFE0000705 (IID), ucf:46610 (fedora)
Note(s): 2005-08-01
Ph.D.
Engineering and Computer Science, School of Computer Science
Doctorate
This record was generated from author submitted information.
Subject(s): Particle selection
HMM
random field
3D reconstruction
Macromolecules
Virology
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000705
Restrictions on Access: campus 2015-01-31
Host Institution: UCF

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