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.