Abstract
Mammographic image analysis is an important tool for the detection and assessment of breast cancer. Previous studies have shown that the performance of image analysis algorithms can be improved by applying them in the retroareolar (RA) region of the breast. However, previous works have relied on subjective, manual segmentation of the RA region. This paper presents a method for the fully-automated detection of the RA region in x-ray mammography images. The method is based on a curvilinear coordinate system that automatically adapts to the breast shape and size. Experiments using logistic regression analysis on images from a publicly available dataset show that the proposed method outperforms the traditional approach in the task of cancer detection.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
American Cancer Society. Breast Cancer Facts and Figures 2015-2016 Atlanta:American Cancer Society, Inc. 2015.
Tahoces P. G., Correa J., Soutos M., Gomez L., Vidal J. J.. Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns Physics in Medicine and Biology. 1995;40:103.
Li Hui, Giger Maryellen L., Huo Zhimin, et al. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location Medical Physics. 2004;31:549–555.
Wei Jun, Chan Heang-Ping, Wu Yi-Ta, et al. Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study Radiology. 2011;260:42–49.
Brandt S. S., Karemore G., Karssemeijer N., Nielsen M.. An Anatomically Oriented Breast Coordinate System for Mammogram Analysis IEEE Transactions on Medical Imaging. 2011;30:1841–1851.
Pertuz S., Julia C., Puig D.. A Novel Mammography Image Representation Framework with Application to Image Registration in 2014 22nd International Conference on Pattern Recognition (ICPR):3292–3297 2014.
Abdel-Nasser Mohamed, Moreno Antonio, Puig Domenec. Temporal mammogram image registration using optimized curvilinear coordinates Computer Methods and Programs in Biomedicine. 2016;127:1–14.
Ballard Dana H. Generalizing the Hough transform to detect arbitrary shapes Pattern recognition. 1981;13:111–122.
Liu Li, Wang Jian, Wang Tianhui. Breast and Pectoral Muscle Contours Detection Based on Goodness of Fit Measure in (iCBBE) 2011 5th International Conference on Bioinformatics and Biomedical Engineering:1–4 2011.
Karssemeijer Nico. Automated classification of parenchymal patterns in mammograms Physics in medicine and biology. 1998;43:365.
Heath Michael, Bowyer Kevin, Kopans Daniel, Moore Richard, Kegelmeyer W Philip. The digital database for screening mammography in Proceedings of the 5th international workshop on digital mammography:212–218Citeseer 2000.
Devijver Pierre A, Kittler Josef. Pattern recognition: A statistical approach;761. Prentice-Hall London 1982.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Torres, G.F., Pertuz, S. (2017). Automatic Detection of the Retroareolar Region in X-Ray Mammography Images. In: Torres, I., Bustamante, J., Sierra, D. (eds) VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016. IFMBE Proceedings, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-10-4086-3_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-4086-3_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4085-6
Online ISBN: 978-981-10-4086-3
eBook Packages: EngineeringEngineering (R0)