Skip to main content
SpringerLink
Log in

Comparison of Validity Indexes for Fuzzy Clusters of fMRI Data

  • Conference paper
  • First Online:
VipIMAGE 2019 (VipIMAGE 2019)

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 34))

  • 859 Accesses

Abstract

In computational neuroimaging, the analysis of functional Magnetic Resonance Images (fMRIs) using fuzzy clustering methods is a promising data driven approach to explore brain functional connectivity. In this complex domain, accurate evaluation procedures based on suitable indexes, able to identify optimal clustering results, are of great values strongly affecting the validity and interpretation of the overall fMRI data analysis. A large number of clustering validation indexes have been proposed in literature. This work proposes a comparison analysis of eight representative fuzzy and crisp clustering validation indexes. Salient aspects of the proposed strategy are the use of the widely adopted fuzzy c-means algorithm as underlying fuzzy clustering algorithm and the use of resting state fMRI data from the NITRC repository.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)

    Article  Google Scholar 

  2. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  3. Biswal, B.B., Mennes, M., Zuo, X., Gohel, S., Kelly, C., Smith, S.M., Beckmann, C.F., Adelstein, J.S., Buckner, R.L., Colcombe, S., et al.: Toward discovery science of human brain function. Proc. Nat. Acad. Sci. 107(10), 4734–4739 (2010)

    Article  CAS  Google Scholar 

  4. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 1(2), 224–227 (1979)

    Article  CAS  Google Scholar 

  5. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)

    Google Scholar 

  6. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Nat. Acad. Sci. 102(27), 9673–9678 (2005)

    Article  CAS  Google Scholar 

  7. Fukuyama, Y.: A new method of choosing the number of clusters for the fuzzy c-mean method. In: 1989 Proceedings of the 5th Fuzzy System Symposium, pp. 247–250 (1989)

    Google Scholar 

  8. Gurrutxaga, I., Muguerza, J., Arbelaitz, O., Pérez, J.M., Martín, J.I.: Towards a standard methodology to evaluate internal cluster validity indices. Pattern Recogn. Lett. 32(3), 505–515 (2011)

    Article  Google Scholar 

  9. Van Den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. neuropsychopharmacol. J. Eur. Coll. Neuropsychopharmacol. 20, 519–534 (2010)

    Article  Google Scholar 

  10. Kennedy, D.N., Haselgrove, C., Riehl, J., Preuss, N., Buccigrossi, R.: The nitrc image repository. Neuroimage 124, 1069–1073 (2016)

    Article  Google Scholar 

  11. Lee, M.H., Hacker, C.D., Snyder, A.Z., Corbetta, M., Zhang, D., Leuthardt, E.C., Shimony, J.S.: Clustering of resting state networks. PLoS ONE 7(7), e40370 (2012)

    Article  CAS  Google Scholar 

  12. Li, H., Zhang, S., Ding, X., Zhang, C., Dale, P.: Performance evaluation of cluster validity indices (CVIs) on Multi/Hyperspectral remote sensing datasets. Remote Sens. 8(4), 295 (2016)

    Article  Google Scholar 

  13. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487–501 (2004)

    Article  Google Scholar 

  14. Rezaee, R.M., Lelieveldt, B.P.F., Reiber, J.H.C.: A new cluster validity index for the fuzzy c-mean. Pattern Recogn. Lett. 19(3–4), 237–246 (1998)

    Article  Google Scholar 

  15. Sun, H., Wang, S., Jiang, Q.: FCM-based model selection algorithms for determining the number of clusters. Pattern Recogn. 37(10), 2027–2037 (2004)

    Article  Google Scholar 

  16. Vergani, A.A., Binaghi, E.: A soft Davies-Bouldin separation measure. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2018)

    Google Scholar 

  17. Wismuller, A.: Model-free functional MRI analysis based on unsupervised clustering. J. Biomed. Inform. 37, 10–18 (2004)

    Article  Google Scholar 

  18. Stoll, G., Meier, D., Valavanis, A., Boesiger, P., Golay, X., Kollias, S.: A new correlation-based fuzzy logic clustering algorithm for fMRI. Magn. Reson. Med. 40(2), 249–260 (1998)

    Article  Google Scholar 

  19. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Theoretical and Applied Science, University of Insubria, Varese, Italy

    Samuele Martinelli & Elisabetta Binaghi

  2. Department of Computer Science, Middlesex University, London, UK

    Alberto Arturo Vergani

Authors
  1. Samuele Martinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Arturo Vergani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elisabetta Binaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samuele Martinelli .

Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  2. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Renato Manuel Natal Jorge

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martinelli, S., Vergani, A.A., Binaghi, E. (2019). Comparison of Validity Indexes for Fuzzy Clusters of fMRI Data. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2019. VipIMAGE 2019. Lecture Notes in Computational Vision and Biomechanics, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-32040-9_18

Download citation

Publish with us

Policies and ethics

Search

Navigation