DOI : https://doi.org/10.18517/ijaseit.7.6.3073

Dental X-ray Image Segmentation using Gaussian Kernel-Based in Conditional Spatial Fuzzy C-means

Arna Fariza (1), Agus Zainal Arifin (2), Eha Renwi Astuti (3)
(1) Institut Teknologi Sepuluh Nopember Politeknik Elektronika Negeri Surabaya
(2) Institut Teknologi Sepuluh Nopember
(3) Airlangga University
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How to cite (IJASEIT) :
Fariza, Arna, et al. “Dental X-Ray Image Segmentation Using Gaussian Kernel-Based in Conditional Spatial Fuzzy C-Means”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, Dec. 2017, pp. 2159-67, doi:10.18517/ijaseit.7.6.3073.
Citation Format :
Dental X-ray image segmentation is a difficult task because of intensity inhomogeneities among various regions, low image quality due to noise and low contrast errors of data scanning. In this paper, we proposed a new conditional spatial fuzzy C-means algorithm with Gaussian kernel function to facilitate dental X-ray image segmentation. The Gaussian kernel function is used as an objective function of conditional spatial fuzzy C-means algorithm to substitute the Euclidian distance. Performance evaluation of the proposed algorithm was carried on dental X-ray from different teeth of some panoramic radiographs. The average of false negative fraction (FNF) and false positive fraction (TPF) values using proposed algorithm better than conditional spatial fuzzy C-means algorithm but vise versa for true positive volume fraction (FPF) value. The segmentation result of the proposed algorithm effectively recognizes tooth region as main part of the dental X-ray image.

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