In pages: 6, International Workshop on Microscopic Image Analysis with Application in Biology (MIAAB), September 2011 (inproceedings)
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.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems