Automatic particle picking using diffusion filtering and random forest 
classification

Joubert, P.; Nickell, S.; Beck, F.; Habeck, M.; Hirsch, M.; Schölkopf, B.

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会議論文

Automatic particle picking using diffusion filtering and random forest classification

MPS-Authors

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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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引用

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).

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

要旨

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.