A Novel Breast Tissue Density Classification Methodology
dc.contributor.author
dc.date.accessioned
2010-05-20T11:36:28Z
dc.date.available
2010-05-03T15:12:39Z
2010-05-20T11:36:28Z
dc.date.issued
2008
dc.identifier.citation
Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E.R.E., et al. (2008). A Novel Breast Tissue Density Classification Methodology. IEEE Transactions on Information Technology in Biomedicine, 12, 1, 55-65. Recuperat 20 maig 2010, a: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4358897
dc.identifier.issn
1089-7771
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dc.description.abstract
It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
IEEE
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Reproducció digital del document publicat a: http://dx.doi.org/10.1109/TITB.2007.903514
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© IEEE Transactions on Information Technology in Biomedicine, 2008, vol. 12, p. 55-65
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Articles publicats (D-ATC)
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Tots els drets reservats
dc.subject
dc.title
A Novel Breast Tissue Density Classification Methodology
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.identifier.doi