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Automatic particle picking using diffusion filtering and random forest classification

2011

Conference Paper

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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.

Author(s): Joubert, P. and Nickell, S. and Beck, F. and Habeck, M. and Hirsch, M. and Schölkopf, B.
Pages: 6
Year: 2011
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: International Workshop on Microscopic Image Analysis with Application in Biology (MIAAB 2011)
Event Place: Heidelberg, Germany

Digital: 0

Links: PDF
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BibTex

@inproceedings{JoubertNBHHS2011,
  title = {Automatic particle picking using diffusion filtering and random forest classification},
  author = {Joubert, P. and Nickell, S. and Beck, F. and Habeck, M. and Hirsch, M. and Sch{\"o}lkopf, B.},
  pages = {6},
  month = sep,
  year = {2011},
  month_numeric = {9}
}