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.