Keywords
Particle selection, HMM, random field, 3D reconstruction, Macromolecules, Virology
Abstract
Three dimensional reconstruction of large macromolecules like viruses at resolutions below 8 Ã… - 10 Ã… 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.
Notes
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Graduation Date
2005
Semester
Summer
Advisor
Marinescu, Dan
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0000705
URL
http://purl.fcla.edu/fcla/etd/CFE0000705
Language
English
Release Date
August 2015
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Singh, Vivek, "Contributions To Automatic Particle Identification In Electron Micrographs: Algorithms, Implementation, And Applications" (2005). Electronic Theses and Dissertations. 4456.
https://stars.library.ucf.edu/etd/4456