Mid Sweden University

Accurate image segmentation is crucial in digital image processing, enabling efficient image analysis and robust vision systems. However, segmentation is a complex task as images vary in their characteristics, and the computational costs increase with the number of classes involved. To address these challenges, incorporating metaheuristic algorithms to guide the segmentation process presents an exciting opportunity for improvement. This research paper introduces a novel multilevel image segmentation approach that leverages a hybrid battle royale optimization algorithm. By combining opposition-based learning, highly disruptive polynomial mutation, differential evolution mutation, and crossover operators, the proposed method enhances the original battle royale optimization algorithm and effectively solves the segmentation problem. To evaluate the effectiveness of the proposed approach, the minimum cross-entropy criterion is applied to two sets of reference images that undergo multilevel thresholding with up to five thresholds. The results are compared with those obtained using nine other metaheuristic algorithms, employing various image quality metrics such as peak signal noise ratio, structural similarity index method, feature similarity index method, quality index based on local variance, Haar wavelet-based perceptual similarity index, and universal image quality index. The results are analyzed quantitatively, qualitatively, and statistically. The findings demonstrate the potential of the proposed approach in achieving high-quality multilevel thresholding image segmentation. Additionally, the hybrid battle royale optimization algorithm showcases its robustness and efficiency when compared to the other metaheuristic algorithms tested. Notable results are PSNR = 2.13E+01, SSIM = 8.41E-01, FSIM = 8.42E-01, QILV = 8.94E-01, HPSI = 6.52E-01, and UIQI = 9.78E-01.