Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm

  • Conference paper
  • First Online:
Nature Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 652))

Abstract

There is a partitioning of a data set X into c-clusters in clustering analysis. In 1984, fuzzy c-mean clustering was proposed. Later, fuzzy c-mean was used for the segmentation of medical images. Many researchers work to improve the fuzzy c-mean models. In our paper, we proposed a novel intuitionistic possibilistic fuzzy c-mean algorithm. Possibilistic fuzzy c-mean and intuitionistic fuzzy c-mean are hybridized to overcome the problems of fuzzy c-mean. This proposed clustering approach holds the positive points of possibilistic fuzzy c-mean that will overcome the coincident cluster problem, reduces the noise and brings less sensitivity to an outlier. Another approach of intuitionistic fuzzy c-mean improves the basics of fuzzy c-mean by using intuitionistic fuzzy sets. Our proposed intuitionistic possibilistic fuzzy c-mean technique has been applied to the clustering of the mammogram images for breast cancer detector of abnormal images. The experiments result in high accuracy with clustering and breast cancer detection.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cyber. 9, 62–66 (1979)

    Google Scholar 

  2. Krishnapuram, R., Keller, J.M.: A possibilistic approach of clustering model. IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)

    Article  Google Scholar 

  3. Pal, N.R., Pal, K., Bezdek, J. C.: A mixed c-means clustering model. Interrnational Conference Fuzzy Systems, Spain, pp. 11–21 (1997)

    Google Scholar 

  4. Pal, N.R., Pal, K., Keller, J. M., Bezdek, J.C.: A possibilistic fuzzy c-mean clustering algorithm. 13 (2005)

    Google Scholar 

  5. de Carvalho, F.D.A.: Fuzzy c-mean clustering for symbolic interval data. Pattern Recogn. Lett. (2006)

    Google Scholar 

  6. Ji, Z.X., Sun, Q.S., Xia, D.S.: A modified possibilistic fuzzy c-means clustering algorithm for bias MR images. Comput. Med. Imaging Graph. 35, 383–397 (2011)

    Google Scholar 

  7. Treerattanapitak, K., Juruskulchai, C.: Outlier Detection with possibilistic exponential fuzzy clustering. 8th FSKD proceeding, pp. 453–457 (2011)

    Google Scholar 

  8. Wahid, A., Gao, X., Andreae, P.: Multi-view clustering of web documents using multi-objective genetic algorithm. IEEE Congress on Evolutionary Computation (2014)

    Google Scholar 

  9. Attanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  Google Scholar 

  10. Chaira, T.: A novel intuitionistic fuzzy c-means clustering algorithm and its application to medical images. Appl. Soft. Comput. 11, 1711–1717 (2011)

    Article  Google Scholar 

  11. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium Mathematical Statistics and Probability. vol. 1, pp. 281–297 (1966)

    Google Scholar 

  12. Treerattanapitak, K., Juruskulchai, C.: Possibilistic exponential fuzzy clustering. J. Comput. Sci. Technol. 28, 311–322 (2013)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, India

    Chiranji Lal Chowdhary

  2. School of Computing Science and Engineering, VIT University, Vellore, India

    D. P. Acharjya

Authors
  1. Chiranji Lal Chowdhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. P. Acharjya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chiranji Lal Chowdhary .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

  2. Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi, Delhi, India

    M. N. Hoda

  3. Department of Computer Science and IT, University of Jammu, Jammu, Jammu and Kashmir, India

    Vinod Sharma

  4. Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi, Delhi, India

    Shivendra Goel

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Chowdhary, C.L., Acharjya, D.P. (2018). Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm. In: Panigrahi, B., Hoda, M., Sharma, V., Goel, S. (eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6747-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6746-4

  • Online ISBN: 978-981-10-6747-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation