Image Binarization using Intensity Range of Grayscale Images

Abstract

The performance of binarization algorithms is determined by the selection of threshold value for binarization, and most of the previous binarization algorithms analyze the intensity distribution of the original images by using the histogram and determine the threshold value using the mean value of intensity or the intensity value corresponding to the valley of the histogram. The previous algorithms could not get the proper threshold value in the case that doesn't show the bimodal characteristic in the intensity histogram or for the case that tries to separate the feature area from the original image. Therefore, this paper proposed a novel algorithm for image binarization, which, first segments the intensity range of grayscale images to several intervals and calculates a mean value of intensity for each interval, and then repeats the interval integration until getting the final threshold value. The interval integration of two neighborhood intervals calculates the ratio of the distances between mean value and adjacent boundary value of two intervals and determine as the threshold value of the new integrated interval the intensity value that divides the distance between mean values of two intervals according to the ratio. The experiment for performance evaluation showed that the proposed algorithm generates the more effective threshold value than the previous algorithms.