Source:http://linkedlifedata.com/resource/pubmed/id/19387513
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
3
|
| pubmed:dateCreated |
2009-5-11
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| pubmed:abstractText |
The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investigating the development of tumor microvessels. The main contribution consists in proposing a machine learning methodology to segment automatically these MRI data, by isolating tumor areas with different meaning, in a histological sense.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
0026-1270
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| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
48
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
248-53
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| pubmed:meshHeading | |
| pubmed:year |
2009
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| pubmed:articleTitle |
DCE-MRI data analysis for cancer area classification.
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| pubmed:affiliation |
Department of Computer Science, University of Verona, Verona, Italy. umberto.castellani@univr.it
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| pubmed:publicationType |
Journal Article
|