Skip to main content

Feature Extraction of Cervical Pap Smear Images Using Fuzzy Edge Detection Method

  • Conference paper
  • First Online:
Data Engineering and Intelligent Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 542 ))

Abstract

In Medical field Segmentation of Medical Images is significant for disease diagnose. Image Segmentation divide an image into regions precisely which helps to identify the abnormalities in the Cancer cells for accurate diagnosis. Edge detection is the basic tool for Image Segmentation. Edge detection identifies the discontinuities in an image and locates the image intensity changes. In this paper, an improved Edge detection method with the Fuzzy approach is proposed to segment Cervical Pap Smear Images into Nucleus and Cytoplasm. Four important features of Cervical Pap Smear Images are extracted using proposed Edge detection method. The accuracy of extracted features using proposed method is analyzed and compared with other popular Image Segmentation techniques.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Sulaimana, S.N., Mat-Isab, N.A., Othmanc, N.H., Ahmada, F.: Improvement of features extraction process and classification of cervical cancer for the neuralpap system. Procedia Comput. Sci. 60, 750–759 (2015)

    Google Scholar 

  2. Sajeena, T.A., Jereesh, A.S.: Cervical cancer detection through automatic segmentation and classification of pap smear cells. Int. J. Appl. Eng. Res. 10(18), 39078–39084 (2015). ISSN 0973-4562

    Google Scholar 

  3. Muthukrishnan, R., Radha, M.: Edge detection techniques for image segmentation. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 3(6) (2011)

    Google Scholar 

  4. El Emary, I.M.M.: On the application of artificial neural networks in analyzing and classifying the human chromosomes. J. Comput. Sci. 2(1), 72–75 (2006)

    Article  Google Scholar 

  5. Mondal, K., Dutta, P., Bhattacharyya, S.: Efficient fuzzy rule base design using image features for image extraction and segmentation. In: Fourth International Conference on Computational Intelligence and Communication Networks (2012)

    Google Scholar 

  6. Mustafa, N., Mat Isa, N.A., Mashor, M.Y., Othman, N.H.: New Features of Cervical Cells for Cervical Cancer Diagnostic System Using Neural Network (2007)

    Google Scholar 

  7. Ghafar, R., Mat-Isa, N.A., Ngah, U.K., Mashor, M.Y., Othman, N.H.: Segmentation of stretched pap smear cytology images using clustering algorithm. In: CDROM Proceedings of World Congress on Medical Physics and Biomedical Engineering (WC2003). Paper no. 2356, vol. 4, Sydney, Australia. 24–29 (2003)

    Google Scholar 

  8. Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: Seeded region growing features extraction algorithm; its potential use in improving screening for cervical cancer. Int. J. Comput. Internet Manag. 13(1) (2004)

    Google Scholar 

  9. Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation—a survey of soft computing approaches. Int. J. Recent Trends Eng. Technol. 1(2) (2009)

    Google Scholar 

  10. Tan, K.S., Isa, N.A.M.: Color image segmentation using histogram thresholding—Fuzzy C-meanshybrid approach. Pattern Recogn. (Impact Factor: 3.1). 01/2011; 44(1), 1–15 (2011). doi:10.1016/j.patcog.2010.07.013

  11. Mehena, J., Adhikary, M.C.: Medical image edge detection based on neuro-fuzzy approach. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Automa. Control Inf. Eng. 10(1) (2016)

    Google Scholar 

  12. Mondal, K., Dutta, P., Bhattacharyya, S.: Efficient fuzzy rule base design using image features for image extraction and segmentation. In: Fourth International Conference on Computational Intelligence and Communication Networks (2012)

    Google Scholar 

  13. Prasanna, M.K., Rai, S.C.: Fuzzy logic—a comprehensive study. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1(10) (2014). ISSN 2348 – 4853

    Google Scholar 

  14. Raj, A., Srivastava, A., Bhateja, V.: Computer aided detection of brain tumor in magnetic resonance images. Int. J. Eng. Technol. (IACSIT-IJET) 3, 523–532 (2011)

    Google Scholar 

  15. Gupta, A., Ganguly, A., Bhateja, V.: A noise robust edge detector for color images using hilbert transform. In: Proceedings of (IEEE) 3rd International Advance Computing Conference, pp. 1207–1212, February (2013)

    Google Scholar 

  16. Dagar, N.S., Dahiya, P.K.: Soft computing techniques for edge detection problem: a state-of-the-art review. Int. J. Comput. Appl. (0975–8887) 136(12) (2016)

    Google Scholar 

  17. Jahne, B., Haubecker, H., Geibler, P.: Handbook of Computer Vision and Applications, vol. 2. Academic Press Publishers (1999)

    Google Scholar 

  18. Divya, B., Shanthi, T.K., Sethuramalingam, T.K.: Edge detection technique by fuzzy logic CLA and canny edge detector using fuzzy image processing. Int. J. Recent Innov. Trends Comput. Commun. 2(4), 954–957 (2014). ISSN 2321–8169

    Google Scholar 

  19. Tizhoosh, H.R.: Fuzzy Image Processing (in German). Springer, Berlin (1997)

    Google Scholar 

  20. Manikandan, G., Sairam, N., Harish, V., Saikumar, N.: Fuzzy logic—a comprehensive study. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1(10), (2014). ISSN 2348 – 4853

    Google Scholar 

  21. dos Santos Schwaab, A.A., Nassar, S.M., de Freitas Filho, P.J.: Automatic methods for generation of type-1 and interval type-2 fuzzy membership functions. J. Comput. Sci. 11(9), 976–987 (2015)

    Google Scholar 

  22. Alikhani, A., Ahmadi, A., Alirezaee, S., Ahmadi, M., Erfani, S.: A CMOS implementation of programmable gaussian fuzzifier. Canadian Conference on Electrical and Computer Engineering (2015)

    Google Scholar 

  23. http://mathbits.com/MathBits/TISection/Statistics2/correlation.htm (2016)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Hemalatha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Hemalatha, K., Usha Rani, K. (2018). Feature Extraction of Cervical Pap Smear Images Using Fuzzy Edge Detection Method. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3223-3_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3222-6

  • Online ISBN: 978-981-10-3223-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics