Show simple item record A. en_US Jordi en_US R. en_US J. en_US E. en_US Erika R. E. en_US Reyer en_US 2008-03-04T12:48:57Z 2008-03-04T12:48:57Z 2008-01-07 en_US
dc.identifier en_US
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_US
dc.identifier.other PURE: 75797 en_US
dc.identifier.other dspace: 2160/520 en_US
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_US
dc.format.extent 11 en_US
dc.relation.ispartof IEEE Transactions on Information Technology in BioMedicine en_US
dc.title A novel breast tissue density classification framework en_US
dc.contributor.pbl Department of Computer Science en_US
dc.contributor.pbl Vision, Graphics and Visualisation Group en_US

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