title: Identification of spherical virus particles in digitized images of entire electron micrographs.
abstract: New methods are described that should facilitate high-resolution (5-10 A) image reconstructions from low-dose, low-contrast electron micrographs of frozen-hydrated specimens and processing of large, digital images produced by new imaging devices and modern electron microscopes. Existing techniques for automatic selection of images of individual biological macromolecules from electron micrographs are inefficient or unreliable. We describe the Crosspoint method (CP), which produces good quality solutions with relatively small miss rates and few false hits, and an extension of this method along with a procedure for refining its solution. Two algorithms for processing large images, one based on image subsampling, the other on image decomposition, are described. A large image is first compressed (e.g., by subsampling) and the CP method is applied to the compressed image to produce an initial solution. The information gathered at this stage is used to cut the original image into subimages and then to refine the particle coordinates in each subimage. An interactive environment for experimenting with particle identification methods is described.
authors.affiliation: Department of Computer Science, Indiana University, South Bend, Indiana 46634, USA.
authors.forename: I M
authors.initials: IM
authors.lastname: Boier Martin
authors.forename: D C
authors.initials: DC
authors.lastname: Marinescu
authors.forename: R E
authors.initials: RE
authors.lastname: Lynch
authors.forename: T S
authors.initials: TS
authors.lastname: Baker
mesh_terms: D000465:Algorithms; D010584:Bacteriophage phi X 174 / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted; D008854:Microscopy, Electron / Q000379:methods*; D012087:Reoviridae / Q000648:ultrastructure; D012984:Software; D014780:Viruses / Q000648:ultrastructure*