Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints
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; ii) Modeling the dental
segmentation problem in the form of
semi-supervised fuzzy clustering with spatial constraints; iii) 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.
| Title: | Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints |
| Authors: | Le Hoang Son Tran Manh Tuan |
| Keywords: | Dental features;Dental image segmentation;Fuzzy clustering;Semi-supervised fuzzy clustering;X-ray images |
| Issue Date: | 2017 |
| Publisher: | PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND |
| Citation: | ISIKNOWLEDGE |
| Abstract: | 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; ii) Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; iii) 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 |
| Description: | TNS06979 ; ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE Volume: 59 Pages: 186-195 Published: MAR 2017 |
| URI: | http://repository.vnu.edu.vn/handle/VNU_123/28304 http://www.sciencedirect.com/science/article/pii/S0952197617300039 |
| ISSN: | 0952-1976 1873-6769 |
| Appears in Collections: | Bài báo của ĐHQGHN trong Web of Science |
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