Linked Life Data

12160706

Source:http://linkedlifedata.com/resource/pubmed/id/12160706

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2002-8-5
pubmed:abstractText
An automatic image segmentation method is used to improve processing and visualization of data obtained by electron microscopy. Exploiting affinity criteria between pixels, e.g., proximity and gray level similarity, in conjunction with an eigenvector analysis, the image is subdivided into areas which correspond to objects or meaningful regions. Extending a proposal by Shi and Malik (1997, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 731-737) the approach was adapted to the field of electron microscopy, especially to three-dimensional application as needed by electron tomography. Theory, implementation, parameter setting, and results obtained with a variety of data are presented and discussed. The method turns out to be a powerful tool for visualization with the potential for further improvement by developing and tuning new affinity.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1047-8477
pubmed:author
pubmed:issnType
Print
pubmed:volume
138
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
105-13
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis.
pubmed:affiliation
Max-Planck-Institut für Biochemie, Am Klopferspitz 18a, D-8215 Martinsried, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't