Linked Life Data

15605419

Source:http://linkedlifedata.com/resource/pubmed/id/15605419

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-12-22
pubmed:abstractText
Autofocusing is a fundamental technology for automated biological and biomedical analyses and is indispensable for routine use of microscopes on a large scale. This article presents a comprehensive comparison study of 18 focus algorithms in which a total of 139,000 microscope images were analyzed. Six samples were used with three observation methods (brightfield, phase contrast, and differential interference contrast (DIC)) under two magnifications (100x and 400x). A ranking methodology is proposed, based on which the 18 focus algorithms are ranked. Image preprocessing was also conducted to extensively reveal the performance and robustness of the focus algorithms. The presented guidelines allow for the selection of the optimal focus algorithm for different microscopy applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1059-910X
pubmed:author
pubmed:copyrightInfo
2004 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
65
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
139-49
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Autofocusing in computer microscopy: selecting the optimal focus algorithm.
pubmed:affiliation
Advanced Micro and Nanosystems Laboratory, University of Toronto, Canada. sun@mie.utoronto.ca
pubmed:publicationType
Journal Article, Evaluation Studies