Detecting particles in cryo-EM micrographs using learned features.

Satya P Mallick; Yuanxin Zhu; David Kriegman

doi:10.1016/j.jsb.2003.11.005

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Detecting particles in cryo-EM micrographs using learned features.

Satya P Mallick·Yuanxin Zhu·David Kriegman

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Abstract

A new learning-based approach is presented for particle detection in cryo-electron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features of the particle's appearance using a set of training examples of the particles and a set of images that do not contain particles. The algorithm is fast (10 s on a 1.3 GHz Pentium M processor), is generic, and is not limited to any particular shape or size of the particle to be detected. The method has been evaluated on a publicly available dataset of 82 cryoEM images of keyhole lympet hemocyanin (KLH). From 998 automatically extracted particle images, the 3-D structure of KLH has been reconstructed at a resolution of 23.2 A which is the same resolution as obtained using particles manually selected by a trained user.

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Algorithms Animals Artificial Intelligence Cryoelectron Microscopy Electronic Data Processing Fourier Analysis Hemocyanins Image Processing, Computer-Assisted Imaging, Three-Dimensional Models, Molecular Mollusca Particle Size Pattern Recognition, Automated Protein Conformation ROC Curve Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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Satya P Mallick, Yuanxin Zhu, David Kriegman. Detecting particles in cryo-EM micrographs using learned features.. https://doi.org/10.1016/j.jsb.2003.11.005

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