Contralateral asymmetry for breast cancer detection: A CADx approach
- 1 January 2018
- journal article
- research article
- Published by Elsevier BV in Biocybernetics and Biomedical Engineering
- Vol. 38 (1), 115-125
- https://doi.org/10.1016/j.bbe.2017.10.005
Abstract
Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. However, most automated detection algorithms do not take it into account. In this research, we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subject-wise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog-and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breast cancer, aiding in the re-scheduling of the radiologists' image queue, an issue of utmost importance in developing countries. (C) 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.Keywords
This publication has 40 references indexed in Scilit:
- Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitrationEuropean Radiology, 2013
- Computed-aided diagnosis (CAD) in the detection of breast cancerEuropean Journal of Radiology, 2012
- Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetryMedical Engineering & Physics, 2011
- Computerized Detection of Breast Tissue Asymmetry Depicted on Bilateral Mammograms: A Preliminary Study of Breast Risk StratificationAcademic Radiology, 2010
- Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval CancerJournal of Digital Imaging, 2010
- A review of automatic mass detection and segmentation in mammographic imagesMedical Image Analysis, 2009
- Rank-Based Inverse Normal Transformations are Increasingly Used, But are They Merited?Behavior Genetics, 2009
- Computer-aided diagnosis in medical imaging: Historical review, current status and future potentialComputerized Medical Imaging and Graphics, 2007
- Automatic Identification of the Pectoral Muscle in MammogramsIEEE Transactions on Medical Imaging, 2004
- Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature spacePhysics in Medicine & Biology, 1995