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 s...

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Detalles Bibliográficos
Autores: 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
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/13157
Acceso en línea:http://hdl.handle.net/10256/13157
Access Level:acceso embargado
Palabra clave:Mama -- Radiografia
Breast -- Radiography
Imatges -- Anàlisi
Image analysis
Imatgeria mèdica
Imaging systems in medicine
Imatges digitals
Digital images
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Descripción
Sumario: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