Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its
simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu’s method, and K-means clustering).
However, the computation time of these algorithms grows exponentially with the number of thresholds due to their
exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a
population of potential solutions and decision-making processes. It has shown considerable success in solving complex
optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be
a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a
balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already
sampled regions. Then, we apply the new DE into the traditional Otsu’s method to shorten the computation time.
Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding
methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of
the traditional Otsu method.