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TY - JOUR AU - Zamudio Arteaga, Melisa Pamela del Rosario AU - Mejía Rodríguez, Aldo Rodrigo AU - Pérez Badillo, Martha Patricia AU - Galván Espinoza, Héctor Alejandro PY - 2021/11/30 Y2 - 2026/03/22 TI - Estimación Semi-automática de Fracción Glandular Mamaria en Imágenes de Mastografía JF - Memorias del Congreso Nacional de Ingeniería Biomédica JA - MCNIB VL - 8 IS - 1 SE - Física Médica y Protección Radiológica DO - UR - https://www.memoriascnib.mx/index.php/memorias/article/view/933 SP - 374-377 AB - <p>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.</p> ER -