An Improved Cuckoo Search Algorithm with Dynamic Parameters and Hybrid Distribution for Enhanced CLAHE
Received: 20 February 2025 | Revised: 9 April 2025, 24 April 2025, and 3 May 2025 | Accepted: 8 May 2025 | Online: 2 August 2025
Corresponding author: Wahyono
Abstract
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.
Keywords:
Contrast Limited Adaptive Histogram Equalization (CLAHE), cuckoo search algorithm, dynamic discovery rate, dynamic step size, hybrid distributionDownloads
References
H. I. Ashiba et al., "Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform," Multimedia Tools and Applications, vol. 78, no. 9, pp. 11277–11290, May 2019. DOI: https://doi.org/10.1007/s11042-018-6545-9
Z. Ullah, S. H. Lee, and D. An, "Histogram Equalization based Enhancement and MR Brain Image Skull Stripping using Mathematical Morphology," International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110372
K. Mayathevar, M. Veluchamy, and B. Subramani, "Fuzzy color histogram equalization with weighted distribution for image enhancement," Optik, vol. 216, Aug. 2020, Art. no. 164927. DOI: https://doi.org/10.1016/j.ijleo.2020.164927
B. Subramani and M. Veluchamy, "Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement," Journal of Medical Systems, vol. 44, no. 6, Jun. 2020, Art. no. 103. DOI: https://doi.org/10.1007/s10916-020-01568-9
D. Susilo and Wahyono, "An Analysis of Image Enhancement Effects on Convolutional Neural Network-based Pulmonary Tuberculosis Detection," E3S Web of Conferences, vol. 465, 2023, Art. no. 02054. DOI: https://doi.org/10.1051/e3sconf/202346502054
Wahyono, "Analisis Pengaruh Image Enhancement Pada Pendeteksian COVID-19 Berbasis Citra X-Ray," Techno.Com, vol. 22, no. 1, pp. 186–194, Feb. 2023. DOI: https://doi.org/10.33633/tc.v22i1.7195
M. J. Alwazzan, M. A. Ismael, and A. N. Ahmed, "A Hybrid Algorithm to Enhance Colour Retinal Fundus Images Using a Wiener Filter and CLAHE," Journal of Digital Imaging, vol. 34, no. 3, pp. 750–759, Jun. 2021. DOI: https://doi.org/10.1007/s10278-021-00447-0
M. Ge, Q. Hong, and L. Zhang, "A Hybrid DCT-CLAHE Approach for Brightness Enhancement of Uneven-illumination Underwater Images," in Proceedings of the 2018 2nd International Conference on Video and Image Processing, Hong Kong, Dec. 2018, pp. 123–127. DOI: https://doi.org/10.1145/3301506.3301539
G. Gao, H. Lai, Y. Liu, L. Wang, and Z. Jia, "Sandstorm image enhancement based on YUV space," Optik, vol. 226, Jan. 2021, Art. no. 165659. DOI: https://doi.org/10.1016/j.ijleo.2020.165659
R. P. R. Chegireddy and A. Srinagesh, "A Novel Method for Human MRI Based Pancreatic Cancer Prediction Using Integration of Harris Hawks Varients & VGG16: A Deep Learning Approach," Informatica, vol. 47, no. 1, May 2023. DOI: https://doi.org/10.31449/inf.v47i1.4433
P. Singh, R. Mukundan, and R. De Ryke, "Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization," Journal of Digital Imaging, vol. 33, no. 1, pp. 273–285, Feb. 2020. DOI: https://doi.org/10.1007/s10278-019-00211-5
B. S. Min, D. K. Lim, S. J. Kim, and J. H. Lee, "A Novel Method of Determining Parameters of CLAHE Based on Image Entropy," International Journal of Software Engineering and Its Applications, vol. 7, no. 5, pp. 113–120, Sep. 2013. DOI: https://doi.org/10.14257/ijseia.2013.7.5.11
B. Joda and Z. Dereboylu, "Digital mammogram enhancement based on automatic histogram clipping," in 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Sep. 2017, pp. 34–38. DOI: https://doi.org/10.1109/CICN.2017.8319351
S. S. M. Sheet, T. S. Tan, M. A. As'ari, W. H. W. Hitam, and J. S. Y. Sia, "Retinal disease identification using upgraded CLAHE filter and transfer convolution neural network," ICT Express, vol. 8, no. 1, pp. 142–150, Mar. 2022. DOI: https://doi.org/10.1016/j.icte.2021.05.002
B. Sree Vidya and E. Chandra, "Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images," Wireless Personal Communications, vol. 106, no. 2, pp. 651–680, May 2019. DOI: https://doi.org/10.1007/s11277-019-06184-6
B. Subramani and M. Veluchamy, "Fuzzy contextual inference system for medical image enhancement," Measurement, vol. 148, Dec. 2019, Art. no. 106967. DOI: https://doi.org/10.1016/j.measurement.2019.106967
S. Mohan and T. R. Mahesh, "Particle Swarm Optimization based Contrast Limited enhancement for mammogram images," in 2013 7th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, Tamil Nadu, India, Jan. 2013, pp. 384–388. DOI: https://doi.org/10.1109/ISCO.2013.6481185
S. Anilkumar, P. R. Dhanya, A. A. Balakrishnan, and M. H. Supriya, "Algorithm for Underwater Cable Tracking Using CLAHE based Enhancement," in 2019 International Symposium on Ocean Technology (SYMPOL), Ernakulam, India, Dec. 2019, pp. 129–137. DOI: https://doi.org/10.1109/SYMPOL48207.2019.9005273
U. Kuran and E. C. Kuran, "Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement," Intelligent Systems with Applications, vol. 12, Nov. 2021, Art. no. 200051. DOI: https://doi.org/10.1016/j.iswa.2021.200051
M. Núñez et al., "Particle swarm optimization applied to parameter tuning of clahe based on entropy and structural similarity index," Journal of Computational Interdisciplinary Sciences, vol. 5, 2014.
K. G. Dhal and S. Das, "Colour retinal images enhancement using modified histogram equalisation methods and firefly algorithm," International Journal of Biomedical Engineering and Technology, vol. 28, no. 2, 2018, Art. no. 160. DOI: https://doi.org/10.1504/IJBET.2018.094725
P. P. Prajapati and M. V. Shah, "Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms," International Journal of Intelligent Systems and Applications, vol. 10, no. 6, pp. 59–67, Jun. 2018. DOI: https://doi.org/10.5815/ijisa.2018.06.07
"ABIDE - Autism Brain Imaging Data Exchange." http://fcon_1000.projects.nitrc.org/indi/abide/.
W. Kun, J. Han, K. M. Abid Ali, and L. Xiaofeng, "Improved Cuckoo Algorithm for Adaptive Adjustment of Discovery Probability," in 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, Jun. 2019, pp. 5873–5878. DOI: https://doi.org/10.1109/CCDC.2019.8833285
M. Reda, M. Elhosseini, A. Haikal, and M. Badawy, "A novel cuckoo search algorithm with adaptive discovery probability based on double Mersenne numbers," Neural Computing and Applications, vol. 33, no. 23, pp. 16377–16402, Dec. 2021. DOI: https://doi.org/10.1007/s00521-021-06236-8
R. C. Gonzalez, Digital Image Processing, 4th ed. Pearson Education, 2019.
S. M. Pizer, J. B. Zimmerman, and E. V. Staab, "Adaptive grey level assignment in CT scan display," Journal of computer assisted tomography, vol. 8, no. 2, pp. 300–305, Apr. 1984.
K. Zuiderveld, "Contrast Limited Adaptive Histogram Equalization," in Graphics Gems, Elsevier, 1994, pp. 474–485. DOI: https://doi.org/10.1016/B978-0-12-336156-1.50061-6
X. S. Yang and S. Deb, "Cuckoo Search via Lévy flights," in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India, 2009, pp. 210–214. DOI: https://doi.org/10.1109/NABIC.2009.5393690
X. S. Yang, Nature-Inspired Optimization Algorithms. Academic Press, 2020. DOI: https://doi.org/10.1016/B978-0-12-821986-7.00013-5
A. Kaveh and T. Bakhshpoori, "Optimum design of steel frames using Cuckoo Search algorithm with Lévy flights," The Structural Design of Tall and Special Buildings, vol. 22, no. 13, pp. 1023–1036, 2013. DOI: https://doi.org/10.1002/tal.754
S. Walton, O. Hassan, K. Morgan, and M. R. Brown, "Modified cuckoo search: A new gradient free optimisation algorithm," Chaos, Solitons & Fractals, vol. 44, no. 9, pp. 710–718, Sep. 2011. DOI: https://doi.org/10.1016/j.chaos.2011.06.004
M. Shehab, A. T. Khader, and M. A. Al-Betar, "A survey on applications and variants of the cuckoo search algorithm," Applied Soft Computing, vol. 61, pp. 1041–1059, Dec. 2017. DOI: https://doi.org/10.1016/j.asoc.2017.02.034
B. Sahu, P. K. Das, and R. Kumar, "A modified cuckoo search algorithm implemented with SCA and PSO for multi-robot cooperation and path planning," Cognitive Systems Research, vol. 79, pp. 24–42, Jun. 2023. DOI: https://doi.org/10.1016/j.cogsys.2023.01.005
S. D. Prestwich, S. A. Tarim, and R. Rossi, "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, vol. 37, no. 2, pp. 708–715, Apr. 2021. DOI: https://doi.org/10.1016/j.ijforecast.2020.08.010
I. Fister, D. Fister, and I. Fister, "A comprehensive review of cuckoo search: variants and hybrids," International Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 4, 2013, Art. no. 387. DOI: https://doi.org/10.1504/IJMMNO.2013.059205
Y. Yuan, L. Wang, Q. Zhou, W. Xiao, L. Wang, and Y. Zhong, "Cuckoo Search Algorithm with Normal Distribution and Its Application in Lychee Image Segmentation," in 2023 9th International Conference on Systems and Informatics (ICSAI), Changsha, China, Dec. 2023, pp. 1–7. DOI: https://doi.org/10.1109/ICSAI61474.2023.10423367
S. H. Anwarininghsih, Wahyono, and R. Sumiharto, "Enhancing Contrast Limited Adaptive Histogram Equalization Using Weighted Sum Cuckoo Search Algorithm," ICIC Express Letters, vol. 19, no. 5, pp. 475–483, 2025.
H. T. R. Kurmasha, A. F. H. Alharan, C. S. Der, and N. H. Azami, "Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2277–2281, Dec. 2017. DOI: https://doi.org/10.48084/etasr.1625
R. Y. Lad, S. Mapari, and F. N. Sibai, "A Novel Approach to Image Classification for Detecting Abnormalities in Neuroimages based on the Structural Similarity Index Measure," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17382–17387, Oct. 2024. DOI: https://doi.org/10.48084/etasr.8384
R. T. Marler and J. S. Arora, "The weighted sum method for multi-objective optimization: new insights," Structural and Multidisciplinary Optimization, vol. 41, no. 6, pp. 853–862, Jun. 2010. DOI: https://doi.org/10.1007/s00158-009-0460-7
L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: A Feature Similarity Index for Image Quality Assessment," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386, Aug. 2011. DOI: https://doi.org/10.1109/TIP.2011.2109730
W. Xue, L. Zhang, X. Mou, and A. C. Bovik, "Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index," IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 684–695, Feb. 2014. DOI: https://doi.org/10.1109/TIP.2013.2293423
D. Asamoah, E. Ofori, S. Opoku, and J. Danso, "Measuring the Performance of Image Contrast Enhancement Technique," International Journal of Computer Applications, vol. 181, no. 22, pp. 6–13, Oct. 2018. DOI: https://doi.org/10.5120/ijca2018917899
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