Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2159-2167 |
| Number of pages | 9 |
| Journal | International Journal on Advanced Science, Engineering and Information Technology |
| Volume | 7 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2017 |
Keywords
- Dental x-ray image
- Fuzzy c-means
- Gaussian kernel-based
- Spatial information