EATHOA: Elite-evolved hiking algorithm for global optimization and precise multi-thresholding image segmentation in intracerebral hemorrhage images.
Authors
Affiliations (6)
Affiliations (6)
- Faculty of Computer and Information Science, Mansoura University, Mansoura, 35516, Egypt. Electronic address: [email protected].
- Faculty of Computers and Information, Minia University, Minia, Egypt; Minia National University, Minia, Egypt. Electronic address: [email protected].
- Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: [email protected].
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, 11671, P.O Box 84428, Riyadh, Saudi Arabia. Electronic address: [email protected].
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
- Department of Mathematics, Saveetha School of Engineering, SIMATS, Thandalam, Chennai, Tamilnadu 602105, India; Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000, Belgrade, Serbia. Electronic address: [email protected].
Abstract
Intracerebral hemorrhage (ICH) is a life-threatening condition caused by bleeding in the brain, with high mortality rates, particularly in the acute phase. Accurate diagnosis through medical image segmentation plays a crucial role in early intervention and treatment. However, existing segmentation methods, such as region-growing, clustering, and deep learning, face significant limitations when applied to complex images like ICH, especially in multi-threshold image segmentation (MTIS). As the number of thresholds increases, these methods often become computationally expensive and exhibit degraded segmentation performance. To address these challenges, this paper proposes an Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), an enhanced version of the Hiking Optimization Algorithm (HOA), specifically designed for high-dimensional and multimodal optimization problems like ICH image segmentation. EATHOA integrates three novel strategies including Elite Opposition-Based Learning (EOBL) for improving population diversity and exploration, Adaptive k-Average-Best Mutation (AKAB) for dynamically balancing exploration and exploitation, and a Turbulent Operator (TO) for escaping local optima and enhancing the convergence rate. Extensive experiments were conducted on the CEC2017 and CEC2022 benchmark functions to evaluate EATHOA's global optimization performance, where it consistently outperformed other state-of-the-art algorithms. The proposed EATHOA was then applied to solve the MTIS problem in ICH images at six different threshold levels. EATHOA achieved peak values of PSNR (34.4671), FSIM (0.9710), and SSIM (0.8816), outperforming recent methods in segmentation accuracy and computational efficiency. These results demonstrate the superior performance of EATHOA and its potential as a powerful tool for medical image analysis, offering an effective and computationally efficient solution for the complex challenges of ICH image segmentation.