Linked Life Data

17354855

Source:http://linkedlifedata.com/resource/pubmed/id/17354855

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2007-3-14
pubmed:abstractText
It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Although different approaches in this area have been proposed in recent years, only a few are based on the BIRADS classification standard. In this paper we review different strategies for extracting features in tissue classification systems, and demonstrate, not only the feasibility of estimating breast density using automatic computer vision techniques, but also the benefits of segmentation of the breast based on internal tissue information. The evaluation of the methods is based on the full MIAS database classified according to BIRADS categories, and agreement between automatic and manual classification of 82% was obtained.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
872-9
pubmed:dateRevised
2009-12-11
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A comparison of breast tissue classification techniques.
pubmed:affiliation
Institute of Informatics and Applications, University of Girona Campus Montilivi, Ed. P-IV, 17071, Girona, Spain. aoliver@eia.udg.es
pubmed:publicationType
Journal Article, Comparative Study, Review, Research Support, Non-U.S. Gov't, Evaluation Studies