Please use this identifier to cite or link to this item: http://repository.vnu.edu.vn/handle/VNU_123/26812
Title: A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation
Authors: Tran, Manh Tuan
Le, Hoang Son
Tran, Thi Ngan
Keywords: Clustering quality;Semi-supervised fuzzy clustering;Interactive fuzzy satisficing;Fuzzy stochastic programming;Dental X-Ray image segmentation
Issue Date: 2016
Publisher: H. : ĐHQGHN
Citation: ISIKNOWLEDGE
Abstract: Dental X-ray image segmentation has an impor-tant role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clus-tering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmenta-tion problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clus-tering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoret-ically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solu-tions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iter-ative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and com-pared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clus-tering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.
Description: APPLIED INTELLIGENCE Volume: 45 Issue: 2 Pages: 402-428 ; TNS06389
URI: http://repository.vnu.edu.vn/handle/VNU_123/26812
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