Detecting particles in cryo-EM micrographs using learned features.
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.
Mallick
Satya P
SP
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093, USA. spmallick@graphics.ucsd.edu
Zhu
Yuanxin
Y
Kriegman
David
D
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D020285:Cryoelectron Microscopy / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods; D005583:Fourier Analysis; D006433:Hemocyanins / Q000737:chemistry / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008958:Models, Molecular; D008974:Mollusca; D010316:Particle Size; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D012372:ROC Curve
D006433:Hemocyanins; C032808:keyhole-limpet hemocyanin