A new gray-level threshold selection method for image segmentation is presented. It is based on minimizing the difference between entropies of the object and the background distributions of the gray-level histogram. The proposed method is similar to the maximum entropy method proposed by Kapur et al. (1985), however, the new method provided a good threshold value in many instances where the previous method did not. The effectiveness of our method is demonstrated by its performance on videomicroscopic images of the rat lung. Extension of the method to higher order probability density functions is described.