  Cryo-electron microscopy (cryo-EM) studies using single particle
reconstruction is extensively used to reveal structural information of
macromolecular complexes. Aiming at the highest achievable resolution, state of
the art electron microscopes acquire thousands of high-quality images. Having
collected these data, each single particle must be detected and windowed out.
Several fully- or semi-automated approaches have been developed for the
selection of particle images from digitized micrographs. However they still
require laborious manual post processing, which will become the major
bottleneck for next generation of electron microscopes. Instead of focusing on
improvements in automated particle selection from micrographs, we propose a
post-picking step for classifying small windowed images, which are output by
common picking software. A supervised strategy for the classification of
windowed micrograph images into particles and non-particles reduces the manual
workload by orders of magnitude. The method builds on new powerful image
features, and the proper training of an ensemble classifier. A few hundred
training samples are enough to achieve a human-like classification performance.
