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A Novel Breast Tissue Density Classification Methodology
Oliver i Malagelada, Arnau; Freixenet i Bosch, Jordi; Martí Marly, Robert; Pont, Josep; Pérez, Elsa; Denton, Erika R. E.; Zwiggelaar, Reyer
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
2010-05-21
Estadística bayesiana
Diagnòstic per la imatge
Imatges -- Segmentació
Imatges mèdiques -- Processament
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Bayesian statistical decision
Breast -- Radiography
Diagnostic imaging
Imaging segmentation
Imaging systems in medicine
Radiography, Medical -- Digital techniques
Tots els drets reservats
Article
IEEE
         

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