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A new approach to the classification of mammographic masses and normal breast tissue

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

© 18th International Conference on Pattern Recognition : 2006 : ICPR 2006, 2006, vol. 4, p. 707-710

IEEE

Author: Oliver i Malagelada, Arnau
Martí Bonmatí, Joan
Martí Marly, Robert
Bosch Rué, Anna
Freixenet i Bosch, Jordi
Date: 2006
Abstract: 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
Format: application/pdf
Citation: Oliver, A., Martí, J., Martí, R., Bosch Rué, A., i Freixenet, J. (2006). A new approach to the classification of mammographic masses and normal breast tissue. 18th International Conference on Pattern Recognition : 2006 : ICPR 2006, 4, 707-710. Recuperat 21 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1699939
ISBN: 0-7695-2521-0
ISSN: 1051-4651
Document access: http://hdl.handle.net/10256/2386
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/ICPR.2006.113
Articles publicats (D-ATC)
Is part of: © 18th International Conference on Pattern Recognition : 2006 : ICPR 2006, 2006, vol. 4, p. 707-710
Rights: Tots els drets reservats
Subject: Diagnòstic per la imatge
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
Title: A new approach to the classification of mammographic masses and normal breast tissue
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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