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A new approach to the classification of mammographic masses and normal breast tissue
Oliver i Malagelada, Arnau; Martí Bonmatí, Joan; Martí Marly, Robert; Bosch Rué, Anna; Freixenet i Bosch, Jordi
A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach
Diagnòstic per la imatge
Imatges mèdiques -- Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
Tots els drets reservats
Artículo
IEEE
         

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