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BioMed Research International
Volume 2015 (2015), Article ID 231656, 12 pages
Research Article

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

1Grupo de Investigación en Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, 64849 Monterrey, NL, Mexico
2Departamento de Investigación e Innovación, Escuela de Medicina, Tecnológico de Monterrey, 64710 Monterrey, NL, Mexico

Received 4 October 2014; Revised 12 June 2015; Accepted 15 June 2015

Academic Editor: Rituraj Purohit

Copyright © 2015 José Celaya-Padilla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (-rank) and -normalizations were used. For the -rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the -normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list.