title: Breast Density Analysis Using an Automatic Density Segmentation Algorithm.
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.
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain. aoliver@eia.udg.edu.
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain.|UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain.
authors.forename: Meritxell
authors.initials: M
authors.lastname: Tortajada
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain.
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.affiliation: UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain.
authors.forename: Sergi
authors.initials: S
authors.lastname: Ganau
authors.affiliation: UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain.
authors.forename: Lidia
authors.initials: L
authors.lastname: Tortajada
authors.affiliation: UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain.
authors.forename: Mariona
authors.initials: M
authors.lastname: Vilagran
authors.affiliation: UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain.
authors.forename: Melcior
authors.initials: M
authors.lastname: Sentís
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain.
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
mesh_terms: D000368:Aged; D000465:Algorithms; D001940:Breast / Q000473:pathology*; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008137:Longitudinal Studies; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results
keywords: Breast tissue density; Computer-assisted image interpretation; Longitudinal studies; Mammography; Segmentation