Estimación Semi-automática de Fracción Glandular Mamaria en Imágenes de Mastografía

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M. P. R. Zamudio-Arteaga
A. R. Mejía-Rodríguez
M. P. Pérez-Badillo
H. A. Galván-Espinoza


Breast cancer is a priority public health problem due to its global magnitude and importance, that develops mainly in the glandular tissue. On mammography imaging, the presence of a large amount of glandular tissue could conceal lesions. Due to this, the estimation of glandular fraction (FG) is a tool that allows evaluating the risk of developing breast cancer. Having knowledge of the different tissues that constitute the anatomy of the breast (glandular, connective and adipose tissues), on a mammography image there are structures that should not be considered for the estimation of the FG, such as skin or pectoral muscle. In the clinical practice, a proper differentiation between glandular and connective tissues is a challenging task, and a discrimination of extra-mammary structures from glandular tissue is particularly difficult due to an intensity similarity. In this work, a strategy to properly isolate the principal breast tissues from the extra-mammary structures, and to perform a robust semi-automatic segmentation of glandular, connective and adipose tissues by using the K-means algorithm in order to provide a quantitative estimation of the mammary glandular fraction is presented. Additionally, a comparison with the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and an empirical glandular fraction estimated by a clinical expert, to demonstrate the convenience of the strategy is made.

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Zamudio Arteaga, M. P. del R., Mejía Rodríguez, A. R., Pérez Badillo, M. P., & Galván Espinoza, H. A. (2021). Estimación Semi-automática de Fracción Glandular Mamaria en Imágenes de Mastografía. Memorias Del Congreso Nacional De Ingeniería Biomédica, 8(1), 374–377. Recuperado a partir de
Física Médica y Protección Radiológica