Thresholding is a way of segmenting an image into foreground and background according to a fixed constant value called a threshold. Image segmentation based on a constant threshold leads to unsatisfactory results with dental X-ray images due to the uneven distribution of pixel intensity. In this paper, an adaptive thresholding method is proposed to attain promising segmentation results for dental X-ray images. The Mean, Median, Midgrey, Niblack, and OTSU thresholding methods are utilized to delineate the acceptable range of threshold values to be applied for segmenting X-ray images. Experimental results showed that the Median method provides consistency in achieving the best range of threshold values which is between 57 and 86 in greyscale.

In this paper, we proposed an age assessment method evaluated on Malaysian children aged between 1 and 17. The approach is based on global fuzzy segmentation, local feature extraction using a projection-based feature transform and a designed deep convolutional neural networks (DCNNs) model. In the first step, a global labelling process was achieved based on fuzzy segmentation, and then, the first-to-third molar teeth were segmented. The deformation invariant features were next extracted based on an intensity projection technique. This technique provided high-order features which were invariant to rotation and partial deformation changes. Finally, the designed DCNN model extracts a large set of features in the hierarchical layers which provided scale, rotation and deformation invariance. The method using this approach was evaluated using a comprehensive and labelled orthopantomographs of 456 patients, which were captured in the Department of Dentistry and Research at Universiti Sains Islam Malaysia. The results from the analysis have suggested that the method can classify the images with high performance, which enabled automated age estimation with high accuracy. � 2019, Springer-Verlag London Ltd., part of Springer Nature.

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. � 2014 IEEE.

The x-ray image is a grey scale 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. � 2014 IEEE.