期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 59, 期 -, 页码 186-195出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2017.01.003
关键词
Dental features; Dental image segmentation; Fuzzy clustering; Semi-supervised fuzzy clustering; X-ray images
In this paper, we propose a novel semi-supervised fuzzy clustering algorithm with spatial constraints for dental segmentation from X-ray images. The detailed contributions include: i) Formulating the spatial features of a dental X-ray image in a dental feature database; Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; Solving the model by the Lagrange multiplier method; iv) Determining the additional information for clustering process by mixing optimal results of Fuzzy C Means with spatial constraints; v) Proposing a novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation. The new algorithm is validated on a real dataset from Hanoi Medical University, Vietnam including 56 dental images. The experimental results reveal that the proposed work has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods. We also suggest the most appropriate values of parameters that should be opted for the algorithm.
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