Linked Life Data

15065673

Source:http://linkedlifedata.com/resource/pubmed/id/15065673

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2004-4-6
pubmed:abstractText
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.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1047-8477
pubmed:author
pubmed:issnType
Print
pubmed:volume
145
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
52-62
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:articleTitle
Detecting particles in cryo-EM micrographs using learned features.
pubmed:affiliation
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093, USA. spmallick@graphics.ucsd.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.