Linked Life Data

19566631

Source:http://linkedlifedata.com/resource/pubmed/id/19566631

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-7-1
pubmed:abstractText
The analysis of cell motion is an essential process in fundamental medical studies because most active cellular functions involve motion. In this paper, a computer-assisted motion analysis system is proposed for cell tracking. In the proposed tracking process, unlike in conventional tracking methods, cellular states referring to the cellular life cycle are defined and appropriate strategies are adopted for cells at different states. The use of cellular state recognition allows detection of possible cell division and hence can improve the robustness of cell tracking. Experimental results show that cells can be successfully segmented and tracked over a long period of time, and the proposed system is found to be as accurate as manual tracking. Various quantitative analyses and visualizations are used to represent cell motion, which demonstrates the usefulness of the proposed system in the study of cell dynamics.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1365-2818
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
235
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
94-105
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Live cell tracking based on cellular state recognition from microscopic images.
pubmed:affiliation
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, R.O.C. ynsun@mail.ncku.edu.tw
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't