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A Geometric Feature Detection Approach to Particle Picking in Electron Micrographs

Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a novel method, based on feature detection, for particle picking. Two fundamental concepts of computational geometry, namely, distance transform and Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with roughly circular or rectangular shapes. Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We will also discuss the extension of this approach to other types of particles with certain geometric features.