Automatic particle picking using diffusion filtering and random forest classification

摘要: An automatic particle picking algorithm for processing electron micrographs of a large molecular complex, the 26S proteasome, is described. The makes use coherence enhancing diffusion filter to denoise data, and random forest classifier removing false positives. It does not make 3D reference model, but uses training set manually picked particles instead. False positive negative rates around 25% 30% are achieved on testing set. was developed specific particle, contains steps that should be useful developing algorithms other particles.