title: [A review of automatic particle recognition in Cryo-EM images].
abstract: Advances in cryo-electron microscopy (Cryo-EM) and single-particle reconstruction have led to increasingly high resolutions of macromolecular three-dimensional reconstruction. However, for keeping up the continuing improvements in resolution, it is necessary to increase the number of particles included in performing reconstructions. Manual selection of particles, even assisted by computer, is a bottleneck of single-particle reconstruction. Cryo-EM image has low signal-to-noise ratio and low contrast, which leads to difficulty in particle picking. Various approaches have been developed to address the problem of automatic particle. This paper describes the application of template-based method, edge based method, feature-based method, neural network, DoG-based and simulated annealing approach in particle picking. The characteristics of various approaches are discussed, and the future development is presented.
authors.affiliation: School of Computer Science & Engineering, South China University of Technology, Guangzhou 510641, China. wxr@mail.gdufs.edu.cn
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Wu
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Wu
mesh_terms: D000818:Animals; D020285:Cryoelectron Microscopy / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D046911:Macromolecular Substances / Q000648:ultrastructure*; D008968:Molecular Conformation; D010316:Particle Size; D012270:Ribosomes / Q000737:chemistry / Q000648:ultrastructure
chemical_list: D046911:Macromolecular Substances