An Improved Cuckoo Search Algorithm with Dynamic Parameters and Hybrid Distribution for Enhanced CLAHE
Sri Huning Anwariningsih, Wahyono Wahyono, Raden Sumiharto · Engineering Technology & Applied Science Research · 2025
Contrast enhancement provides a more precise visualization of anatomical structures, improving diagnostic accuracy in medical images. One of the contrast enhancement methods, Contrast Limited Adaptive Histogram Equalization (CLAHE), often struggles with parameter optimization, leading to suboptimal image quality. Optimal parameter optimization is crucial to balancing contrast enhancement and detail preservation, necessitating robust optimization algorithms. The Cuckoo Search Algorithm (CSA) is well-suited for this task due to its strong global search capabilities and simplicity in handling complex optimization problems. CSA has two parameters, step size and discovery rate, which are often used as constants, resulting in sensitivity to problems, convergence rate, and an optimal solution that cannot be guaranteed simultaneously. To address these limitations, this study proposes an improved CSA, which, unlike conventional CSA with static parameters, introduces dynamic adjustments of the discovery rate ( ) and step size ( ), significantly improving exploration and exploitation capabilities. A hybrid distribution combining normal and uniform distributions is used for cuckoo selection and nest replacement, ensuring a balanced search process. The proposed method, called Dynamic Hybrid CSA (DH-CSA-CLAHE), was tested on MRI images of individuals with autism, showing superiority in MSE, PSNR, AMBE, SSIM, GMSD, and FSIM compared to CSA-CLAHE, PSO-CLAHE, and FA-CLAHE. The experimental results demonstrate the superior performance of the proposed method, achieving average PSNR, SSIM, and FSIM values of 45.54 dB, 0.97, and 0.9995, respectively, indicating excellent structural preservation and image quality. In addition, the method consistently produced the lowest MSE (3.73), AMBE (1.05), and GMSD (0.001) values, confirming its ability to effectively enhance contrast while minimizing distortion. These findings highlight the potential of DH-CSA-CLAHE as an effective tool for medical image preprocessing, contributing to improved diagnostic accuracy in clinical applications.