Skip to main content
SpringerLink
Log in

Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation

  • Conference paper
  • First Online:
Intelligent Computing and Innovation on Data Science

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 248))

  • 787 Accesses

Abstract

Image segmentation is the process of dividing an image into multiple portions and assigning a label to the portions that have similar characteristics. The pulse-coupled neural network (PCNN) models can segment the objects in an image. This paper analyzes the suitability of PCNN models for high-performance biomedical image segmentation. In this research work, three different PCNN models have used to evaluate the performance of classifying the medical images, specifically traditional PCNN, intersecting cortical model PCNN (ICM-PCNN), and unit linking PCNN (UL-PCNN). Various PCNN models were used to extract the essential features from the images and classify the images. The segmentation results obtained by different PCNN models are compared based on entropy, standard deviation, and correlation. The results of PCNN are considered the best-segmented images as it gives the proper output in lesser iteration with the highest entropy, and corresponding standard deviation and correlation are calculated.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Eckhorn R, Reitboeck HJ, Arndt MT, Dicke P (1990) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput 2(3):293–307

    Article  Google Scholar 

  2. Johnson JL, Padgett ML (1999) PCNN models and applications. IEEE Trans Neural Networks 10(3):480–498

    Article  Google Scholar 

  3. Thyagharajan KK, Kalaiarasi G (2018) Pulse coupled neural network based near-duplicate detection of images (PCNN–NDD). Adv Electr Comput Eng 18(3):87–96

    Article  Google Scholar 

  4. Kuntimad G, Ranganath HS (1999) Perfect image segmentation using pulse coupled neural networks. IEEE Trans Neural Networks 10(3):591–598

    Article  Google Scholar 

  5. Gu XD, Guo SD, Yu DH (2002) A new approach for automated image segmentation based on unit-linking PCNN. In: Proceedings of the international conference on machine learning and cybernetics, November 2002, vol 1. IEEE, pp 175–178

    Google Scholar 

  6. Stewart RD, Fermin I, Opper M (2002) Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans Neural Networks 13(6):1557–1562

    Article  Google Scholar 

  7. Yonekawa M, Kurokawa H (2009) An automatic parameter adjustment method of pulse coupled neural network for image segmentation. In: International conference on artificial neural networks, September 2009. Springer, Berlin, Heidelberg, pp 834–843

    Google Scholar 

  8. Berg H, Olsson R, Lindblad T, Chilo J (2008) Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10–12):1980–1993

    Article  Google Scholar 

  9. Ma Y, Qi CL (2006) Study of automated PCNN system based on genetic algorithm. J Syst Simul 18(3):722–725

    Google Scholar 

  10. Bi Y, Qiu T, Li X, Guo Y (2004) Automatic image segmentation based on a simplified pulse coupled neural network. In: International symposium on neural networks, August 2004. Springer, Berlin, Heidelberg, pp 405–410

    Google Scholar 

  11. Broussard RP, Rogers SK, Oxley ME, Tarr GL (1999) Physiologically motivated image fusion for object detection using a pulse coupled neural network. IEEE Trans Neural Networks 10(3):554–563

    Article  Google Scholar 

  12. Zhang J, Hu J (2008) Image segmentation based on 2D Otsu method with histogram analysis. In: International conference on computer science and software engineering, December 2008, vol 6. IEEE, pp 105–108

    Google Scholar 

  13. Li J, Li X, Yang B, Sun X (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Google Scholar 

  14. Karvonen JA (2004) Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks. IEEE Trans Geosci Remote Sens 42(7):1566–1574

    Article  Google Scholar 

  15. Kavitha S, Thyagharajan KK (2017) Efficient DWT-based fusion techniques using genetic algorithm for optimal parameter estimation. Soft Comput 21(12):3307–3316

    Article  Google Scholar 

  16. Kinser JM, Wyman CL, Kerstiens BL (1998) Spiral image fusion: a 30 parallel channel case. Opt Eng 37(2):492–498

    Article  Google Scholar 

  17. Kurokawa H, Kaneko S, Yonekawa M (2008) A color image segmentation using inhibitory connected pulse coupled neural network. In: International conference on neural information processing, November 2008. Springer, Berlin, Heidelberg, pp 776–783

    Google Scholar 

  18. Mureşan RC (2003) Pattern recognition using pulse-coupled neural networks and discrete Fourier transforms. Neurocomputing 51:487–493

    Article  Google Scholar 

  19. Doss S, Paranthaman J, Gopalakrishnan S, Duraisamy A, Pal S, Duraisamy B, Le DN (2021) Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. CMC-Computers Mater Continua 66(2):1577–1594

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

    T. Vignesh

  2. Department of ECE, RMD Engineering College, Kavaraipettai, India

    K. K. Thyagharajan

  3. Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India

    L. Balaji

  4. CSE Department, Sathyabama Institute of Science and Technology, Chennai, India

    G. Kalaiarasi

Authors
  1. T. Vignesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. K. Thyagharajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Balaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Kalaiarasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

  2. Computer Science and Information Engineering, National Cheng Kung University, Taiwan, Taiwan

    Sun-Yuan Hsieh

  3. Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India

    Suseendran Gopalakrishnan

  4. Lincoln University College, Malaysia, Malaysia

    Balaganesh Duraisamy

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vignesh, T., Thyagharajan, K.K., Balaji, L., Kalaiarasi, G. (2021). Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation. In: Peng, SL., Hsieh, SY., Gopalakrishnan, S., Duraisamy, B. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, vol 248. Springer, Singapore. https://doi.org/10.1007/978-981-16-3153-5_10

Download citation

Publish with us

Policies and ethics

Search

Navigation