An Adaptive Thresholding Method for Segmenting Dental X-Ray Images

Abstract

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.