Dynamic semi-supervised fuzzy clustering for dental X-ray image segmentation: an analysis on the additional function

Tran Manh Tuan, Le Hoang Son, Le Ba Dung
Author affiliations

Authors

  • Tran Manh Tuan School of Information and Communication Technology, Thai Nguyen University
  • Le Hoang Son VNU University of Science, Vietnam National University
  • Le Ba Dung Institute of Information Technology, VAST

DOI:

https://doi.org/10.15625/1813-9663/31/4/7234

Keywords:

Additional function, dynamic semi-supervised fuzzy clustering, dental X-ray image segmentation, fuzzy C-Means

Abstract

Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. In our previous work, the authors presented a semi-supervised fuzzy clustering algorithm using interactive fuzzy satisficing named as SSFC-FS for this problem. An important issue of SSFC-FS is that the pre-defined membership matrix is a fixed function in the sense that it uses the same structure and parameters for all dental images. This is a shortcoming of SSFC-FS since each image has its own structure and morphology so that it needs different membership matrices. In this paper, the authors propose another new dynamic semi-supervised fuzzy clustering called SSFC-FSAI that extends SSFC-FS by employing a collection of pre-defined membership matrices for dental images. A procedure to choose a suitable pre-defined membership matrix for a given dental X-ray image is proposed and attached to SSFC-FSAI. Experimental results on a real dataset of 56 dental X-ray images from Hanoi University of Medical in 2014 - 2015 show that SSFC-FSAI has better accuracy than SSFC-FS and the relevant algorithms.

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Published

26-01-2016

How to Cite

[1]
T. M. Tuan, L. H. Son, and L. B. Dung, “Dynamic semi-supervised fuzzy clustering for dental X-ray image segmentation: an analysis on the additional function”, JCC, vol. 31, no. 4, p. 323, Jan. 2016.

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Section

Computer Science

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