Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel method based on modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) applied overcome shortcomings original AEO. main idea proposed method, (AEODE), employ operators DE as local search AEO improve ecosystem solutions. We used benchmark images test performance AEODE, we compared it several existing approaches. AEODE achieved high when evaluated by structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), fitness values. Moreover, outperformed basic version concerning SSIM PSNR 78% 82%, respectively, which reserves best features for each DE.