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Automatic particle picking using diffusion filtering and random forest classification

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84390

Joubert,  P
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83949

Nickell S, Beck F, Habeck,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83969

Hirsch,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Joubert, P., Nickell S, Beck F, Habeck, M., Hirsch, M., & Schölkopf, B. (2011). Automatic particle picking using diffusion filtering and random forest classification. In International Workshop on Microscopic Image Analysis with Application in Biology (MIAAB 2011) (pp. 1-6).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BA3E-A
Zusammenfassung
An automatic particle picking algorithm for processing electron micrographs of a large molecular complex, the 26S proteasome, is described. The algorithm makes use of a coherence enhancing diffusion filter to denoise the data, and a random forest classifier for removing false positives. It does not make use of a 3D reference model, but uses a training set of manually picked particles instead. False positive and false negative rates of around 25 to 30 are achieved on a testing set. The algorithm was developed for a specific particle, but contains steps that should be useful for developing automatic picking algorithms for other particles.