Ítem
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Oliver i Malagelada, Arnau
Freixenet i Bosch, Jordi Martí Marly, Robert Pont, Josep Pérez, Elsa Denton, Erika R. E. Zwiggelaar, Reyer |
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| 5 juny 2018 | |
| 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 | |
| http://hdl.handle.net/2072/320394 | |
| eng | |
| IEEE | |
| Tots els drets reservats | |
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Estadística bayesiana
Diagnòstic per la imatge Imatges -- Segmentació Imatgeria mèdica -- Processament Mama -- Radiografia Radiografia mèdica -- Tècniques digitals Bayesian statistical decision Breast -- Radiography Diagnostic imaging Imaging segmentation Imaging systems in medicine Radiography, Medical -- Digital techniques |
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| A Novel Breast Tissue Density Classification Methodology | |
| info:eu-repo/semantics/article | |
| Recercat |
