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dc.contributor.author Oliver, A.
dc.contributor.author Freixenet, Jordi
dc.contributor.author Marti, R.
dc.contributor.author Pont, J.
dc.contributor.author Perez, E.
dc.contributor.author Denton, Erika R. E.
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2008-03-04T12:48:57Z
dc.date.available 2008-03-04T12:48:57Z
dc.date.issued 2008-01-07
dc.identifier.citation Oliver , A , Freixenet , J , Marti , R , Pont , J , Perez , E , Denton , E R E & Zwiggelaar , R 2008 , ' A novel breast tissue density classification framework ' IEEE Transactions on Information Technology in BioMedicine , vol 12 , no. 1 , pp. 55-65 . , 10.1109/TITB.2007.903514 en
dc.identifier.issn 1089-7771
dc.identifier.other PURE: 75797
dc.identifier.other dspace: 2160/520
dc.identifier.uri http://hdl.handle.net/2160/520
dc.description Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., Zwiggelaar, R. (2008). A novel breast tissue density classification framework. IEEE Transactions on Information Technology in BioMedicine, 12 (1), 55-65 en
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. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof IEEE Transactions on Information Technology in BioMedicine en
dc.title A novel breast tissue density classification framework en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1109/TITB.2007.903514
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Peer reviewed en


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