title: Particle finding in electron micrographs using a fast local correlation algorithm.
abstract: A versatile tool for selecting particles from electron micrographs, intended for single particle analysis and three-dimensional reconstruction, is presented. It is based on a local real-space correlation method. Real-space correlations calculated over a local area are suitable for finding small objects or patterns in a larger field. They provide a very sensitive measure-of-fit, partly due to local optimisation of the numerical scaling. It is equivalent to least squares with optimised scaling between the two objects being correlated. The only disadvantage of real-space methods is that they are slow to compute. A fast local correlation algorithm based on Fourier transforms has been developed, which is approximately two orders of magnitude faster than the explicit real-space formulation. The algorithm is demonstrated by application to the problem of locating images of macromolecules in transmission electron micrographs of unstained frozen hydrated specimens. This is a challenging computational problem because these images have low contrast and a low signal-to-noise ratio. Picking particles by hand is very time consuming and can be less accurate. The automated procedure gives a significant increase in speed, which is important if large numbers of particles have to be picked.
authors.affiliation: MRC-Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK. roseman@mrc-lmb.cam.ca.uk
authors.forename: Alan M
authors.initials: AM
authors.lastname: Roseman
mesh_terms: D000465:Algorithms; D002244:Carbon / Q000032:analysis; D005583:Fourier Analysis; D008854:Microscopy, Electron / Q000379:methods*; D010316:Particle Size; D012270:Ribosomes / Q000648:ultrastructure; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D002244:Carbon