Various techniques have previously been proposed for thresholding of images to separate objects from the background. Although these thresholding techniques have been proven effective on particular types of images, none of them is able to produce consistently good results on a wide range of existing images. The nonextensive cross-entropy (also known as Tsallis cross-entropy) is introduced to determine the optimal threshold value. The new thresholding scheme aims to minimize the Tsallis cross-entropy between the original image and the thresholded image. The effectiveness of the proposed scheme is demonstrated by using examples from the synthetic images, natural scene images, and an image dataset that includes nondestructive testing images and document images, on the basis of comparison with the traditional cross-entropy, Otsu’s, minimum error thresholding, and two state-of-the-art methods. Furthermore, a tunable parameter q of Tsallis cross-entropy in the presented scheme gives the proposed methods the potential to handle the different image segmentation tasks.