Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

• Published in: Expert systems with applications Vol. 83; pp. 242 - 256
• Main Authors: Aziz, Mohamed Abd El, Ewees, Ahmed A., Hassanien, Aboul Ella
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.10.2017; Elsevier BV
• Subjects: Algorithms; Digital imaging; Fitness; Image processing systems; Image segmentation; Moth-Flame Optimization (MFO); Multilevel; Multilevel thresholding; Optimization; Thresholds; Variance analysis; Whale Optimization Algorithm (WOA); Image segmentation; Multilevel thresholding; Moth-Flame Optimization (MFO); Whale Optimization Algorithm (WOA)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.04.023

•Two metaheuristic algorithms (WOA and MFO) are used.•These algorithms are applied to multilevel thresholding image segmentation.•MFO and WOA are better than compared algorithms.•MFO is better than WOA for higher number of thresholds. Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsu’s fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.