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A Novel Semi-supervised Fuzzy Clustering Method Based on Interactive Fuzzy Satisficing for Dental X-ray Image Segmentation by Tran Manh Tuan; Tran Thi Ngan; Le Hoang Son is a Computer Science article available to read on EtoBox.

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Dental X-ray image segmentation has an important 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 clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation 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 clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzystochastic environment, is applied to get the cluster centers

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Author

Tran Manh Tuan; Tran Thi Ngan; Le Hoang Son

Publisher

Springer US; Springer-Verlag; Kluwer Academic Publishers; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 0924-669X)

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)