Biomedical Engineering Letters

, Volume 9, Issue 4, pp 481–496 | Cite as

A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm

  • Santhos Kumar Avuti
  • Varun Bajaj
  • Anil KumarEmail author
  • Girish Kumar Singh
Original Article


Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur’s and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.


Computer aided diagnosis (CAD) Electro-magnetism optimization algorithm (EMO) Mammogram images Multilevel thresholding Pectoral muscle segmentation Kapur’s and Otsu method 


Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.


  1. 1.
    Suckling JP. The mammographic image analysis society digital mammogram database. Digital Mammo, pp 375–86, July 1994.Google Scholar
  2. 2.
    Gupta R, Undrill PE. The use of texture analysis to delineate suspicious masses in mammography. Phys Med Biol. 1995;40(5):835–55.CrossRefGoogle Scholar
  3. 3.
    Hatanaka Y, et al. Development of an automated method for detecting mammographic masses with a partial loss of region. IEEE Trans Med Imaging. 2001;20(12):1209–14.CrossRefGoogle Scholar
  4. 4.
    Karssemeijer N. Automated classification of parenchymal patterns in mammograms. Phys Med Biol. 1998;43(2):365–78.CrossRefGoogle Scholar
  5. 5.
    Saha PK, et al. Breast tissue density quantification via digitized mammograms. IEEE Trans Med Imaging. 2001;20(8):792–803.CrossRefGoogle Scholar
  6. 6.
    Ferrari RJ, et al. Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc. In: Proceedings of 5th international workshop on digital mammography. Toronto, Canada; 2000.Google Scholar
  7. 7.
    Kwok SM, et al. Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans Med Imaging. 2004;23(9):1129–40.CrossRefGoogle Scholar
  8. 8.
    Chandrasekhar R, Attikiouzel Y. New range-based neighbourhood operator for extracting edge and texture information from mammograms for subsequent image segmentation and analysis. IEE Proc Sci Meas Technol. 2000;147(6):408–13.CrossRefGoogle Scholar
  9. 9.
    Raba D, et al. Breast segmentation with pectoral muscle suppression on digital mammograms. In: Iberian Conference on pattern recognition and image analysis. Springer, Berlin, pp 471–8; June 2005.CrossRefGoogle Scholar
  10. 10.
    Yapa RD, Harada K. Breast skin-line estimation and breast segmentation in mammograms using fast-marching method. Int J Biol Biomed Med Sci. 2008;3(1):54–62.Google Scholar
  11. 11.
    Karnan M, Thangavel K. Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of micro-calcifications. Comput Methods Programs Biomed. 2007;87(1):12–20.CrossRefGoogle Scholar
  12. 12.
    Mustra M, Grgic M, Rangayyan RM. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput. 2016;54(7):1003–24.CrossRefGoogle Scholar
  13. 13.
    Rampun A, et al. Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artif Intell Med. 2017;79:28–41.CrossRefGoogle Scholar
  14. 14.
    Mustra M, Grgic M. Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Sig Process. 2013;93(10):2817–27.CrossRefGoogle Scholar
  15. 15.
    Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern SMC. 1979;9:62–6.CrossRefGoogle Scholar
  16. 16.
    Kapur JN, Sahoo PK, Wong AKC. A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process. 1985;2:273–85.CrossRefGoogle Scholar
  17. 17.
    Oliva D, et al. Multilevel thresholding segmentation based on harmony search optimization. J Appl Math 2013;2013:1–24.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Poli R, et al. Particle swarm optimization: an overview. Swarm Intell. 2007;1(1):33–57.CrossRefGoogle Scholar
  19. 19.
    Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NMF. An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl. 2012;39(16):12407–17.CrossRefGoogle Scholar
  20. 20.
    Olivaa Diego, et al. A multilevel thresholding algorithm using electromagnetism optimization. Neuro-computing. 2014;139:357–81.Google Scholar
  21. 21.
    Horng M. Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl. 2011;38:13785–91.Google Scholar
  22. 22.
    Pal SK, Bhandari D, Kundu MK. Genetic algorithms, for optimal image enhancement. Pattern Recognit Lett. 1994;15:261–71.CrossRefGoogle Scholar
  23. 23.
    Sathya PD, Kayalvizhi R. Optimal multilevel thresholding using bacterial foraging algorithm. Expert Syst Appl. 2011;38(12):15549–64.CrossRefGoogle Scholar
  24. 24.
    Kumar AS, et al. Fractional-order darwinian swarm intelligence inspired multilevel thresholding for mammogram segmentation. In: 2018 (ICCSP). IEEE, 2018.Google Scholar
  25. 25.
    İlker Birbil S, Fang S-C. An electromagnetism-like mechanism for global optimization. J Glob Optim. 2003;25:263–82.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Cowan EW. Basic electromagnetism. New York: Academic Press; 1968.Google Scholar

Copyright information

© Korean Society of Medical and Biological Engineering 2019

Authors and Affiliations

  • Santhos Kumar Avuti
    • 1
  • Varun Bajaj
    • 1
  • Anil Kumar
    • 1
    Email author
  • Girish Kumar Singh
    • 2
  1. 1.PDPM Indian Institute of Information Technology Design and ManufacturingJabalpurIndia
  2. 2.Department of Electrical EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

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