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Automatic microcalcification and cluster detection for digital and digitised mammograms

In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image

We would like to thank the reviewers for their critical evaluation of the manuscript. This study has been supported by the Ministerio de Ciencia e Innovacion under grants TIN2011-23704 and AYA2010-21782-C03-02. A.Torrent holds a FPU grant AP2007-01934. M. Tortajada holds a UdG grant BRGR10-04

© Knowledge-Based Systems, 2012, vol. 28, p. 68-75

Elsevier

Director: Ministerio de Ciencia e Innovación (Espanya)
Autor: Oliver i Malagelada, Arnau
Torrent Palomeras, Albert
Lladó Bardera, Xavier
Tortajada Giménez, Meritxell
Tortajada, Lídia
Sentís, Melcior
Freixenet i Bosch, Jordi
Zwiggelaar, Reyer
Data: 1 abril 2012
Resum: In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image
We would like to thank the reviewers for their critical evaluation of the manuscript. This study has been supported by the Ministerio de Ciencia e Innovacion under grants TIN2011-23704 and AYA2010-21782-C03-02. A.Torrent holds a FPU grant AP2007-01934. M. Tortajada holds a UdG grant BRGR10-04
Format: application/pdf
Cita: 015232
ISSN: 0950-7051
Accés al document: http://hdl.handle.net/10256/11350
Llenguatge: eng
Editor: Elsevier
Col·lecció: MICINN/PN 2012-2012/TIN2011-23704
MICINN/PN 2011-2013/AYA2010-21782-C03-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.knosys.2011.11.021
Articles publicats (D-ATC)
És part de: © Knowledge-Based Systems, 2012, vol. 28, p. 68-75
Drets: Tots els drets reservats
Matèria: Mama -- Radiografia
Breast -- Radiography
Imatges -- Anàlisi
Image analysis
Imatgeria mèdica
Imaging systems in medicine
Títol: Automatic microcalcification and cluster detection for digital and digitised mammograms
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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