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Breast Density Analysis Using an Automatic Density Segmentation Algorithm

Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density

This work was partially funded by the Spanish R+D+I grant no. TIN2012-37171-C02-01

© Journal of Digital Imaging, 2015, vol. 28, núm. 5, p. 604-612

Springer Verlag

Manager: Ministerio de Economía y Competitividad (Espanya)
Author: Oliver i Malagelada, Arnau
Tortajada Giménez, Meritxell
Lladó Bardera, Xavier
Freixenet i Bosch, Jordi
Ganau, Sergi
Tortajada, Lídia
Vilagran, Mariona
Sentís, Melcior
Martí Marly, Robert
Date: 2015 February 27
Abstract: Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density
This work was partially funded by the Spanish R+D+I grant no. TIN2012-37171-C02-01
Format: application/pdf
Citation: 023267
ISSN: 0897-1889 (versió paper)
1618-727X (versió electrònica)
Document access: http://hdl.handle.net/10256/13157
Language: eng
Publisher: Springer Verlag
Collection: MINECO/PN 2013-2015/TIN2012-37171-C02-01
Reproducció digital del document publicat a: http://dx.doi.org/10.1007/s10278-015-9777-5
Articles publicats (D-ATC)
Is part of: © Journal of Digital Imaging, 2015, vol. 28, núm. 5, p. 604-612
Rights: Tots els drets reservats
Subject: Mama -- Radiografia
Breast -- Radiography
Imatges -- Anàlisi
Image analysis
Imatgeria mèdica
Imaging systems in medicine
Imatges digitals
Digital images
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Title: Breast Density Analysis Using an Automatic Density Segmentation Algorithm
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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