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

doi:10.15625/1813-9663/31/4/7234

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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