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A Novel Breast Tissue Density Classification Methodology

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

© IEEE Transactions on Information Technology in Biomedicine, 2008, vol. 12, p. 55-65

IEEE

Author: Oliver i Malagelada, Arnau
Freixenet i Bosch, Jordi
Martí Marly, Robert
Pont, Josep
Pérez, Elsa
Denton, Erika R. E.
Zwiggelaar, Reyer
Date: 2008
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
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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
ISSN: 1089-7771
Document access: http://hdl.handle.net/10256/2351
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/TITB.2007.903514
Articles publicats (D-ATC)
Is part of: © IEEE Transactions on Information Technology in Biomedicine, 2008, vol. 12, p. 55-65
Rights: Tots els drets reservats
Subject: 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
Title: A Novel Breast Tissue Density Classification Methodology
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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