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One of the most popular techniques for image segmentation is known as multilevel thresholding. The main difference between multilevel and binary thresholding, is that the binary thresholding outputs a two-color image, usually black and white, while the multilevel thresholding outputs a grey scale image in which more details from the original picture can be kept. Two major problems with using the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take exceptionally long computational time; defining a proper number of thresholds or levels that will keep most of the relevant details from the original image is a difficult task. In this study a new approach based on the Kullback-Leibler information distance, also known as Relative Entropy, is proposed. The approach minimizes a mathematical model, which will determine the number of thresholds …