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


  • 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




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


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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. Bezdek, J. C., Ehrlich, R., & Full, W., “FCM: The fuzzy c-means clustering algorithm,” Computers & Geosciences, 10(2), 191-203,1984 .

. Bouchachia, A., & Pedrycz, W., “Data clustering with partial supervision,” Data Mining and Knowledge Discovery, 12(1), 47-78, 2006.

. Chen, A., Wittman, T., Tartakovsky, A., & Bertozzi, A., “Image segmentation through efficient boundary sampling,” In SAMPTA'09, 2009, pp. Special-session.

. Gould, S., Gao, T., & Koller, D., “Region-based segmentation and object detection,” In Advances in neural information processing systems, 2009, pp. 655-663.

. Leung, T., & Malik, J., “Contour continuity in region based image segmentation,” In Computer Vision—ECCV'98 , Springer Berlin Heidelberg, 544-559,1998.

. Li, S., Fevens, T., Krzyżak, A., & Li, S., ”An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments,” Computerized Medical Imaging and Graphics, 30(2), 65-74, 2006.

. Mohan, C., & Nguyen, H. T., “An interactive satisficing method for solving multiobjective mixed fuzzy-stochastic programming problems,” Fuzzy sets and Systems, 117(1), 61-79, 2006.

. Narkhede, H. P., “Review of image segmentation techniques,” Int. J. Sci. Mod. Eng, 1(5461), 28, 2013.

. Nayak, J., Naik, B., & Behera, H. S., “Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014,” In Computational Intelligence in Data Mining-Volume 2 Springer India, 133-149, 2015.

. Ngo, L. T., Mai, D. S., & Pedrycz, W., “Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection,” Computers & Geosciences, 83, 1-16, 2015.

. Nomir, O., & Abdel-Mottaleb, M., ”A system for human identification from X-ray dental radiographs,” Pattern Recognition, 38(8), 1295-1305, 2005

. Otsu, N., “A threshold selection method from gray-level histograms,” Automatica, 11(285-296), 23-27, 1975.

. Rad, A. E, Rahim, M. S. M, & Norouzi, A., “Level Set and morphological Operation Techniques in Application of Dental Image Segmentation,” International Scholarly and Scientific Research & Innovation, 8(4), 177-180, 2014.

. Soumi Ghosh, Sanjay Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy CMeans Algorithms”, (IJACSA) International Journal of Advanced Computer Science and Applications, 4(4), 35-39, 2013.

. Vendramin, L., Campello, RJ, & Hruschka, ER., “Relative clustering validity criteria: A comparative overview,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 3(4), 209-235, 2010.

. Yasunori, E., Yukihiro, H., Makito, Y., & Sadaaki, M., ”On semi-supervised fuzzy c-means clustering,” In Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on IEEE, 1119-1124, 2009.

. Yin, X., Shu, T., & Huang, Q., ”Semi-supervised fuzzy clustering with metric learning and entropy regularization,” Knowledge-Based Systems, 35, 304-311, 2012.

. Zhu, G., Zhang, S., Zeng, Q., & Wang, C., “Boundary-based image segmentation using binary level set method,” Optical Engineering, 46(5), 050501-050501, 2007.

. Tuan, T.M., Ngan, T.T. & Son, L.H., ” A Novel Semi-Supervised Fuzzy Clustering Method based on Interactive Fuzzy Satisficing for Dental X-Ray Image Segmentation,” Applied Intelligence, in press.




How to Cite

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.



Computer Science

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