Adaptive threshold works at pixel level and the result of the adaptive threshold is either background or foreground. The adaptive threshold produces superior result compared to global threshold, especially for the images that have uneven pixel intensity distribution. In the dental age assessment, X-ray image is used as an aid to estimate the age of the person. The existing process of assessment is done manually. However, this can be made automatically. The process of automated dental age assessment, require threshold segmentation to separate the background and the teeth area. In order to optimize the result of the adaptive threshold, it depends on the threshold value. In this paper, we present three methods (i.e. mean, median and OTSU) to estimate the range of the threshold value. The result of the study shown that the median threshold provides better results than the mean and OTSU thresholds. In terms of the region of the segmentation, median threshold value covers more teeth followed by mean threshold and OTSU threshold. The region of segmentation is important because one of the requirements in Demirjian method is to assess all the teeth types in quadrant 2 and quadrant 3. Based on the result of the experiment shown the region of median threshold able to segment most of the teeth area in quadrant 2 and quadrant 3.

The x-ray image is a greyscale image and the distribution of the intensity of the pixel is uneven. The x-ray image widely use in dental age assessment especially Demirjian method. The purpose of the dental age assessment is to estimate the age of unidentified bodies. The current process is done manually by the examiner. The process potentially converted to an automated system. The development an automated dental age assessment required segmentation process; which is dividing the image into multiple meaningful parts based on region and edge. The edge segmentation form a contour based on the links detected. The authors present two types of edge segmentation methods (i.e Sobel and Canny). The objective of the study is to make a comparison between the two methods. Result showed Sobel method was able to segment all the teeth area and remove the noise on the x-ray image while Canny algorithm was not able to segment all the teeth area especially incisors. The region of segmentation is important because one of the requirements in Demirjian method is to assess all the teeth types in quadrant 2 and quadrant 3. Based on the result, the experiment showed the Sobel algorithm able to segment most of the teeth area in quadrant 2 and quadrant 3.