Abstract
The pectoral muscle is the high-intensity region in most mediolateral oblique (MLO) views of mammograms. Since it appears at the same intensity as most abnormalities it should be removed for successful classification. Removal of pectoral muscle is often a challenging task since its position, size and shape are different for different patients and it may not occur at all. In this paper, an efficient technique for the detection and removal of pectoral muscle is proposed. The algorithm is tested and proved efficient over a wide range of pectoral muscle types and datasets based on IOU and RMSE value.
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Abeje S, Seme A, Tiblet A (2019) Factors associated with breast cancer screening awareness and practices of women in addis ababa, ethiopia. BMC Women’s Health 19(4):1–8
Angelov P, Sadeghi-Tehran P, Ramezani R (2010) An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving takagi-sugeno fuzzy systems. Int J Intell Syst 26(3):189–205
Arafa A, El-Sokary N, Asad A, Hefny H (2019) Computer-aided detection system for breast cancer based on GMM and SVM. Arab J Nucl Sci Appl 52(2):142–150
Boss RSC, Thangavel K, Daniel D, Arul P (2013) Automatic mammogram image breast region extraction and removal of pectoral muscle. Int J Sci Eng Res 4(5):1–8
Charate AP, Jamge SB (2017) The preprocessing methods of mammogram images for breast cancer detection. Int J Recent Innov Trends Comput Commun 5(1):261–264
Chen C, Liu G, Sudlow G, Wang J (2014) Shape-based automatic detection of pectoral muscle boundary in mammograms. J Med Biol Eng 5:315–322
Sissons C (2018) What does breast cancer look like on a mammogram? In: Medical news today. https://www.medicalnewstoday.com/articles/322068.php. Accessed 10 Feb 2019
Debelee TG, Amirian M, Ibenthal A, Palm G, Schwenker F (2018) Classification of mammograms using convolutional neural network based feature extraction. LNICST 244:89–98
Debelee TG, Gebreselasie A, Schwenker F, Amirian M, Yohannes D (2019) Classification of mammograms using texture and cnn based extracted features. J Biom Biomater Biomed Eng 42:79–97
Debelee TG, Schwenker F, Rahimeto S, Yohannes D (2019) Evaluation of modified adaptive k-means segmentation algorithm. Comput Vis Media. https://doi.org/10.1007/s41095-019-0151-2
Makandar A, Halalli B (2016) Pre-processing of mammography image for early detection of breast cancer. Int J Comput Appl 144(3):11–15
Mughal B, Muhammad N, Sharif M, Rehman A, Saba T (2018) Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer 18:1–14. https://doi.org/10.1186/s12885-018-4638-5
Sargana AB, Angelov P, Habib Z (2017) A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl Sci 7:110 (01)
Sheba KU, Raj Gladston S (2018) An approach for automatic lesion detection in mammograms. Cogent Eng 5(1444320):1–16
Slavkovic-Ilic M, Gavrovska A, Milivojevic M et al (2016) Breast region segmentation and pectoral muscle removal in mammograms. Telfor Journal 8:50–55. https://doi.org/10.5937/telfor1601050s
Toshpulatov Z, Bria A (2018) Pectoral-Muscle-Segmentation-using-Watershed-algorithm. In: GitHub. https://github.com/zafaruzmedtec/Pectoral-Muscle-Segmentation-using-Watershed-algorithm. Accessed 16 Mar 2019
Vaidehi K, Subashini TS (2013) Automatic identification and elimination of pectoral muscle in digital mammograms. Int J Comput Appl 75(14):15–18
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Rahimeto, S., Debelee, T.G., Yohannes, D. et al. Automatic pectoral muscle removal in mammograms. Evolving Systems 12, 519–526 (2021). https://doi.org/10.1007/s12530-019-09310-8
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DOI: https://doi.org/10.1007/s12530-019-09310-8