IDEAS home Printed from https://ideas.repec.org/a/igg/jncr00/v10y2021i1p1-14.html
   My bibliography  Save this article

An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation

Author

Listed:
  • B. S. Harish

    (JSS Science and Technology University, India)

  • M. S. Maheshan

    (JSS Science and Technology University, India)

  • C. K. Roopa

    (JSS Science and Technology University, India)

  • S. V. Aruna Kumar

    (Faculty of Engineering and Technology, M.S. Ramaiah University of Applied Sciences, India)

Abstract

This article performs the sclera segmentation task by proposing a new hybrid symbolic fuzzy c-means (HSFCM) clustering method. Practically, though the data point exhibits some sort of similarity, unfortunately they are not undistinguishable and exhibit some sort of dissimilarity. Thus, to capture these disparities, the proposed work uses symbolic interval valued representation method. Further, to handle uncertainty and imprecision, the paper has proposed to use symbolic fuzzy clustering methods. To assess the performance of the proposed method, extensive experimentation is conducted on SSRBC2016 dataset. The proposed clustering method is compared with existing FCM, KFCM, RSKFCM method in terms of cluster validity indices and accuracy. The obtained outcomes demonstrated that the proposed method performed better compared to the contemporary methods.

Suggested Citation

  • B. S. Harish & M. S. Maheshan & C. K. Roopa & S. V. Aruna Kumar, 2021. "An Unsupervised Hybrid Symbolic Fuzzy Clustering Approach for Efficient Sclera Segmentation," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 10(1), pages 1-14, January.
    • Handle: RePEc:igg:jncr00:v:10:y:2021:i:1:p:1-14
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJNCR.2021010101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jncr00:v:10:y:2021:i:1:p:1-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.