Texture-based analysis of clustered microcalcifications detected on mammograms

Authors:
Alain Tiedeu;Christian Daul;Aude Kentsop;Pierre Graebling;Didier Wolf
Affiliations:
LETS, GRETMAT, Ecole Nationale Supérieure Polytechnique, BP 8390, Yaoundé, Cameroon and The Abdus Salam International Centre for Theoretical Physics, Strada Costeria 11, 34014 Trieste, I ...;Centre de Recherche en Automatique de Nancy (CRAN, UMR 7039 CNRS/Nancy University), 2, Avenue de la Forêt de Haye, 54516 Vanduvre-lès-Nancy, France;LETS, GRETMAT, Ecole Nationale Supérieure Polytechnique, BP 8390, Yaoundé, Cameroon;Laboratoire des Sciences de lImage, de lInformatique et de la Télédétection, LSIIT-CNRS UMR 7005, Boulevard Sébastien-Brant, 67400 Illkirch-Graffenstaden, France;Centre de Recherche en Automatique de Nancy (CRAN, UMR 7039 CNRS/Nancy University), 2, Avenue de la Forêt de Haye, 54516 Vanduvre-lès-Nancy, France
Venue:
Digital Signal Processing
Year:
2012

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Abstract

The need for early detection of breast cancer has led to establishing screening programs that generate large volumes of mammograms to be analyzed. These analysis are time consuming and labor intensive. Computerized analysis of mammograms has been suggested as ''second opinion'' or ''pre-reader''. In this paper, we suggest a texture-based computerized analysis clusters of microcalcifications detected on mammograms in order to classify them into benign and malignant types. The test of the proposed system yielded a sensitivity of 100%, a specificity of 87.77% and a good classification rate of 89%; the area under the fitted ROC-curve using the MedCalc Statistical Software was 0.968.