Skip to main content
SpringerLink
Log in

Bio-inspired Optimization Algorithms for Segmentation and Removal of Interphase Cells from Metaphase Chromosomes Images

  • Chapter
  • First Online:
Machine Learning Paradigms: Theory and Application

Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

  • 1663 Accesses

Abstract

Interphase cells are undivided and the condensed mass of chromosomes. They can highly decrease the efficiency of automatic karyotype. karyotype is a test that is used to examine chromosomes. It includes counting the number of chromosomes and finding the structural changes in chromosomes. Manual karyotyping is a very challenging task and very time-consuming. This is due to the color similarity between interphase cells, chromosomes and parts of the background. In this chapter, two automatic approaches are proposed. The first approach is based on using Fast Fuzzy C-Means and Grey Wolf Optimization. The second approach is based on hybrid particle swarm optimization and K-Means algorithm. The proposed approaches are used to remove residual stains, interphase cells and to extract chromosomes from metaphase chromosomes image. The general architecture comprised of three fundamental phases: (1) Preprocessing, (2) Image segmentation based on either hybrid Particle Swarm Optimization and K-Means algorithm or Fast Fuzzy C-Means and Grey Wolf Optimization and (3) Post processing phase. 40 chromosomal images taken from albino rat bone marrow are used in this experiment. The experimental results showed the efficiency of the proposed segmentation and removal approaches. The proposed approach based on Fast Fuzzy C-Means and Grey Wolf Optimization obtained overall 94% accuracy. While, the proposed approach based on Particle Swarm Optimization and K-Means algorithm obtained overall 95% accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abraham, A., Guo, H., Hongb, L.: Swarm intelligence: foundations, perspectives and applications. In: Swarm Intelligent Systems, pp. 3–25 (2006)

    Google Scholar 

  2. Alam, S., Dobbie, G., Riddle, P.: An evolutionary particle swarm optimization algorithm for data clustering. In: Swarm Intelligence Symposium, pp. 21–23. IEEE (2008)

    Google Scholar 

  3. Biniaz, A., Shmasi, M., Abbasi, A.: A novel fast fuzzy C-means clustering technique for segmentation of human brain magnetic resonance images. Off. J. Turk. Fuzzy Syst. Assoc. 4(1), 34–47 (2013)

    Google Scholar 

  4. Dama, A., Todmal, R.: Number plate recognition using improved segmentation. Int. J. Innov. Res. Stud. (IJIRS) 2162–2168 (2013)

    Google Scholar 

  5. El Mahdy, M., Eldin, T., Aly, H.S., Mohammed, F.F., Shaalan, M.I.: Evaluation of hepatotoxic and genotoxic potential of silver nanoparticles in albino rats. Exp. Toxicol. Pathol. (Elsevier) 6721–6729 (2014)

    Google Scholar 

  6. Gaber, T., Sayed, G., Anter, A., Soliman, M., Ali, M., Semary, N. et al.: Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy C-means algorithm. In: Conference Proceedings IEEE Engineering in Medicine and Biology Society, pp. 4254–4257 (2015)

    Google Scholar 

  7. Ghamisi, P., Couceiro, M., Benediktsson, J., Ferreira, N.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. (Elsevier) 39, 12407–12417 (2012)

    Article  Google Scholar 

  8. Guimaraes, L.V., Schuck, A., Elbern, A.: Segmentation of chromosome images. In: The 25th Silver Anniversary International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 941–943. IEEE (2003)

    Google Scholar 

  9. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  10. Ji, L.: Intelligent splitting in the chromosome domain. Pattern Recognit. 22(5), 519–532 (1989)

    Article  Google Scholar 

  11. Karvelis, P.S., Fotiadis, D.I., Syrrou, M., Georgiou, I.: Segmentation of chromosome images based on a recursive watershed transform. In: Proceedings of the 3rd European Medical and Biological Engineering Conference, EMEBC05, vol. 11, pp. 1–5 (2005)

    Google Scholar 

  12. Katz, S.W., Brink, A.D.: Segmentation of chromosome images. In: Proceedings of the IEEE South African Symposium on Communication, Networking and Broadcasting, pp. 85–90 (1993)

    Google Scholar 

  13. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks (ICNN), vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  14. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf optimizer. Adv. Eng. Softw. (Elsevier) 6946–6961 (2014)

    Google Scholar 

  15. Rajaraman, S., Chokkalingam, A.: Connected components labeling and extraction based interphase removal from chromosome images. Int. J. Bio-Sci. Bio-Technol. 5(1), 81–90 (2013)

    Google Scholar 

  16. Sayed, G., Darwish, A., Hassanien, A.: Quantum multi verse optimization algorithm for optimization problems. Neural Comput. Appl. 1–17 (2017)

    Google Scholar 

  17. Sayed, G., Hassanien, A.: Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf optimizer. In: 11th International Computer Engineering Conference (ICENCO), pp. 261–266 (2015)

    Google Scholar 

  18. Sayed, G., Hassanien, A., Azar, A.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 1–18 (2017)

    Google Scholar 

  19. Sayed, G., Soliman, M., Hassanien, A.: Bio-inspired swarm techniques for thermogram breast cancer detection. In: Dey, N., Bhateja, V., Hassanien, A. (eds.) Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, pp. 487–506. Springer International Publishing, Cham (2016)

    Chapter  Google Scholar 

  20. Singh, N., Singh, D.: The improved K-means with particle swarm optimization the improved K-means with particle swarm optimization. J. Inf. Eng. Appl. 3 (2013), ISSN: 2224-5782

    Google Scholar 

  21. Wright, K.: Stedmans medical dictionary. Clin. Nurse Spec. 17(2), 1–15 (2003)

    Google Scholar 

  22. Yosida, T.H., Amano, K.: Autosomal polymorphism in laboratory bred and wild norway rats, rattus norvegicus, found in misima. Chromosoma (Springer-Verlag) 16(6), 658–667 (1965)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

  2. Scientific Research Group in Egypt (SRGE), Giza, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

Authors
  1. Gehad Ismail Sayed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gehad Ismail Sayed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sayed, G.I., Hassanien, A.E. (2019). Bio-inspired Optimization Algorithms for Segmentation and Removal of Interphase Cells from Metaphase Chromosomes Images. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_22

Download citation

Publish with us

Policies and ethics

Search

Navigation