Skip to main content
SpringerLink
Log in

Classification of Skeletal Muscle Fiber Types Using Image Segmentation

  • Conference paper
  • First Online:
Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 573))

  • 598 Accesses

Abstract

Muscle fibers can be classified in a variety of ways based on their different anatomical and histochemical features and these many classifications usually depend on subjective observations and may contradict each other. Thus, an objective method of grouping is always preferred to have a standardized reference for this complicated human tissue. Microscopic images with appropriate staining techniques are proven to be highly reliable in studying the histology of human body, with different image segmentation techniques are applied on these types of images and provide an encouraging outcome to further analyze the composition of the human tissues. In this study, Fuzzy C-Means algorithm was applied on muscle specimen images stained by alpha naphthyl acetate esterase (ANAE) with different number of muscle fibers within the specimen and different reaction times used for the staining step. The results shown were recommended by anatomical experts to rely on and to further develop with other types of tissues. Unsupervised classification techniques based on fuzzy c-means clustering algorithm have proved successful in segmenting of biopsy image of human skeletal muscle tissue into different fiber types.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Kugelberg, E., Edström, L.: Differential histochemical effects of muscle contractions on phosphorylase and glycogen in various types of fibres: relation to fatigue. J. Neurol. Neurosurg. Psychiatry 31(5), 415–423 (1968)

    Article  Google Scholar 

  2. Bottinelli, R., Reggiani, C.: Human skeletal muscle fibres: molecular and functional diversity. Prog. Biophys. Mol. Biol. 73(2–4), 195–262 (2000)

    Article  Google Scholar 

  3. Scott, W., Stevens, J., Binder-Macleod, S.A.: Human skeletal muscle fiber type classifications. Phys. Ther. 81(11), 1810–1816 (2001)

    Article  Google Scholar 

  4. Sjostrom, B.E.M., Friden, J.: Fine structural details of human muscle fibres after fibre type specific glycogen depletion. Histochemistry, 425–438 (1982)

    Google Scholar 

  5. Wilson, J.M., Loenneke, J.P., Jo, E., Wilson, G.J., Zourdos, M.C., Kim, J.S.: The effects of endurance, strength, and power training on muscle fiber type shifting. J. Strength Cond. Res. 26(6), 1724–1729 (2012)

    Article  Google Scholar 

  6. Wang, Y., Winters, J., Subramaniam, S.: Functional classification of skeletal muscle networks. I. Normal physiology. J. Appl. Physiol. 113(12), 1884–1901 (2012)

    Article  Google Scholar 

  7. Kinugawa, S., Tsutsui, H.: Skeletal muscle abnormalities in heart failure. Int. Heart J. 56(5), 475–484 (2015)

    Article  Google Scholar 

  8. Joyce, N.C., Oskarsson, B., Jin, L.W.: Muscle biopsy evaluation in neuromuscular disorders. Phys. Med. Rehabil. Clin. N. Am. 23(3), 609–631 (2012)

    Article  Google Scholar 

  9. Aydin, M.F., Celik, I., Sur, E., Oznurlu, Y., Telatar, T.: Enzyme histochemistry of the peripheral blood lymphocytes in arabian horses. J. Anim. Vet. Adv. 9(5), 920–924 (2010)

    Article  Google Scholar 

  10. Cai, C.: Nonspecific esterase. PathologyOutlines.com website. https://www.pathologyoutlines.com/topic/stainsnonspecificesterase.html. Accessed 16 June 2022

  11. Mohammed, R., Ajwad, A.: CT image segmentation based on clustering methods. J. Fac. Med. Baghdad 52(2), 232–236 (2010)

    Google Scholar 

  12. Pednekar, A., Kakadiaris, I.A., Kurkure, U.: Adaptive fuzzy connectedness-based medical image segmentation. Indian Conf. Comput. Vision, Graph. Image Process. (2002)

    Google Scholar 

  13. Bensaid, A.M., Hall, L.O., Clarke, L.P., Velthuizen, R.P.: MRI segmentation using supervised and unsupervised methods. Proc. Annu. Conf. Eng. Med. Biol. 13(pt 1), 60–61 (1991)

    Google Scholar 

  14. Khamiss, N.N.: Unsupervised segmentation method for brain MRI based on fuzzy techniques. Nahrain Univ. Coll. Eng. J. 13(1), 108–115 (2010)

    Google Scholar 

  15. Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019). https://doi.org/10.1007/s10278-019-00227-x

    Article  Google Scholar 

  16. Yoon, C.H., Bones, P.J., Millane, R.P.: Image analysis for electron microscopy of muscle fibres. Proc. Digit. Imaging Comput. Tech. Appl. DICTA 2005, vol. 2005, no. Dicta, pp. 551–557 (2005)

    Google Scholar 

  17. Papastergiou, P.T.A., Hatzigaidas, A., Cheva, A.: A sophisticated edge detection method for muscle biopsy image analysis. Proc. 7th WSEAS International Conference Signal, Speech Image Process, pp. 118–123 (2007)

    Google Scholar 

  18. Liu, F., Mackey, A.L., Srikuea, R., Esser, K.A., Yang, L.: Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections. J. Microsc. 252(3), 275–285 (2013)

    Article  Google Scholar 

  19. Sertel, O., Dogdas, B., Chiu, C.S., Gurcan, M.N.: Muscle histology image analysis for sarcopenia : registration of successive sections with distinct atpase activity * Dept. of electrical and computer engineering, The Ohio State University, Columbus, OH ( USA ) Dept . of Biomedical Informatics, The O. Science (80):1423–1426 (2010)

    Google Scholar 

  20. Rahmati, M., Rashno, A.: Automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers. Sci. Rep. 11(1) (2021)

    Google Scholar 

  21. Vu, Q.D. et al.: Methods for segmentation and classification of digital microscopy tissue images. Front. Bioeng. Biotechnol. 7 (2019)

    Google Scholar 

  22. Achouri, A., Melizi, M., Belbedj, H., Azizi, A.: Comparative study of histological and histo-chemical image processing in muscle fiber sections of broiler chicken. J. Appl. Poult. Res. 30(3), 100173 (2021)

    Article  Google Scholar 

  23. Janssens, T., Antanas, L., Derde, S., Vanhorebeek, I., Van den Berghe, G., Güiza Grandas, F.: Charisma: an integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting. Med. Image Anal. 17(8), 1206–1219 (2013)

    Google Scholar 

  24. Nguyen, B.P., Heemskerk, H., So, P.T.C., Tucker-Kellogg, L.: Superpixel-based segmentation of muscle fibers in multi-channel microscopy. BMC Syst. Biol. 10(Suppl 5) (2016)

    Google Scholar 

  25. Cui, L., Feng, J., Zhang, Z., Yang, L.: High throughput automatic muscle image segmentation using parallel framework. BMC Bioinformatics 20(1), 1–9 (2019)

    Article  Google Scholar 

  26. Cui, L., Feng, J., Yang, L.: Towards fine whole-slide skeletal muscle image segmentation through deep hierarchically connected networks. J. Healthc. Eng. 2019 (2019)

    Google Scholar 

Download references

Acknowledgment

The study was done in both Al-Nahrain University and Dijlah University College.

Author information

Authors and Affiliations

  1. Department of Medical Instrumentation Techniques Engineering, Dijlah University College, Baghdad, Iraq

    Mehdy Mwaffeq Mehdy

  2. Department Medical Laboratory Techniques, Dijlah University College, Baghdad, Iraq

    Sarah Raad Mohammed

  3. Department of Information and Communication Engineering, Al-Nahrain University, Baghdad, Iraq

    Nasser N. Khamiss

  4. Deparment of Anatomy, College of Medicine, Al-Nahrain University, Baghdad, Iraq

    Anam R. Al-Salihi

Authors
  1. Mehdy Mwaffeq Mehdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarah Raad Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nasser N. Khamiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anam R. Al-Salihi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdy Mwaffeq Mehdy .

Editor information

Editors and Affiliations

  1. Department of Business Analytics, Sunway University, Subang Jaya, Selangor, Malaysia

    Mohammed A. Al-Sharafi

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Al Buraimi University College, Al Buraimi, Oman

    Mohammed Naji Al-Kabi

  4. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mehdy, M.M., Mohammed, S.R., Khamiss, N.N., Al-Salihi, A.R. (2023). Classification of Skeletal Muscle Fiber Types Using Image Segmentation. In: Al-Sharafi, M.A., Al-Emran, M., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems . ICETIS 2022. Lecture Notes in Networks and Systems, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-031-20429-6_58

Download citation

Publish with us

Policies and ethics

Search

Navigation