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Automatic particle picking using diffusion filtering and random forest classification

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Joubert,  P.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Habeck,  M.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Hirsch,  M.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  B.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

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).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-4C56-5
Abstract
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.