Abstract
Mammography is a worldwide image modality used to diagnose breast cancer and can be used to measure breast density (BD). In clinical routine, radiologist perform image evaluations through BIRADS assessment. However, this method has inter and intraindividual variability. An automatic method to measure BD could relieve radiologist’s workload by providing a first aid opinion. However, pectoral muscle (PM) is a high density tissue, with the same imaging characteristics as fibroglandular tissues, which makes its automatic detection a challenging task. The aim of this work is to develop an automatic algorithm to segment and extract PM in digital mammograms. A hybrid methodology has been developed using Hough transform, to find the edge of the PM, and active contour, to segment PM muscle. Seed of active contour is applied automatically in the edge of PM found by Hough transform. An experienced radiologist manually performed the PM segmentation. Manual and automatic methods were compared using the Jaccard index and Bland-Altman statistics. The comparison between methods presented a Jaccard similarity coefficient greater than 90% for all analyzed images. The Bland-Altman statistics compared the segmented PM area and showed agreement between both methods within 95% confidence interval. The method proved to be accurate and robust, segmenting rapid and free of intra and inter-observer variability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu, L.J., et al., Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm. Phys Med Biol, 2012. 57(21): p. 6903–27.
Gubern-Merida, A., et al., Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One, 2014. 9(1): p. e85952.
Wang, J., et al., Agreement of mammographic measures of volumetric breast density to MRI. PLoS One, 2013. 8(12): p. e81653.
Seo, J.M., et al., Automated volumetric breast density estimation: a comparison with visual assessment. Clin Radiol, 2013. 68(7): p. 690–5.
van der Waal, D., et al., Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting. PLoS One, 2015. 10(9): p. e0136667.
Alonzo-Proulx, O., R.A. Jong, and M.J. Yaffe, Volumetric breast density characteristics as determined from digital mammograms. Phys Med Biol, 2012. 57(22): p. 7443–57.
Kallenberg, M.G., et al., Automatic breast density segmentation: an integration of different approaches. Phys Med Biol, 2011. 56(9): p. 2715–29.
van Engeland, S., et al., Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging, 2006. 25(3): p. 273–82.
Feudjio, C.K., et al., Automatic extraction of pectoral muscle in the MLO view of mammograms. Phys Med Biol, 2013. 58(23): p. 8493–515.
Vikhe, P.S. and V.R. Thool, Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter. J Med Syst, 2017. 41(12): p. 190.
Ganesan, K., et al., Pectoral muscle segmentation: a review. Comput Methods Programs Biomed, 2013. 110(1): p. 48–57.
Kwok, S.M., et al., Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans Med Imaging, 2004. 23(9): p. 1129–40.
Wang, L., et al., Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. Journal of Zhejiang University SCIENCE C, 2010. 11(2): p. 111–118.
Ferrari, R., et al., Identification of the breast boundary in mammograms using active contour models. Med Biol Eng Comput., 2004. 42(2): p. 201–208.
Ferrari, R.J., et al., Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging, 2004. 23(2): p. 232–45.
Saltanat, N., M.A. Hossain, and M.S. Alam, An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms. IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications, 2010: p. 1510–1517.
Taghanaki, S.A., et al., Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views. IEEE Trans Biomed Eng, 2017. 64(11): p. 2662–2671.
Chan, T.F. and L.A. Vese, Active contours without edges. IEEE Trans Image Process, 2001. 10(2): p. 266–77.
Mustra, M., M. Grgic, and R.M. Rangayyan, Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput, 2016. 54(7): p. 1003–24.
Rampun, A., et al., Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artif Intell Med, 2017. 79: p. 28–41.
Pavan, A.L.M., et al., Fibroglandular Tissue Quantification in Mammography by Optimized Fuzzy C-Means with Variable Compactness. Innovation and Research in BioMedical engineering, 2017. 48(4): p. 228–233.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that there is no conflict of interest for this study.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pavan, A.L.M., Vacavant, A., Alves, A.F.F., Trindade, A.P., de Pina, D.R. (2019). Automatic Identification and Extraction of Pectoral Muscle in Digital Mammography. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_27
Download citation
DOI: https://doi.org/10.1007/978-981-10-9035-6_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9034-9
Online ISBN: 978-981-10-9035-6
eBook Packages: EngineeringEngineering (R0)