Transactions on Machine Intelligence

Transactions on Machine Intelligence

Medical Image Denoising Using the Cuckoo Optimization Algorithm

Document Type : Original Article

Author
A. Aliabadian
Assistant Professor, Department of Electrical and Biomedical Engineering, Shomal University
10.22034/tmi.2025.244853
Abstract
Medical images acquired using imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound constitute essential sources of information for clinical diagnosis, treatment planning, and quantitative biomedical analysis. Nevertheless, these images are inevitably affected by different types of noise and acquisition-related degradations, which can obscure anatomical structures, distort intensity information, and adversely affect subsequent image-processing and diagnostic tasks. Consequently, effective noise reduction while preserving diagnostically important structures remains a fundamental challenge in medical image processing. Among conventional denoising techniques, the bilateral filter has attracted considerable attention because of its ability to suppress noise while preserving edges by jointly incorporating spatial proximity and intensity similarity into the filtering process. Despite these advantages, the performance of the bilateral filter is highly dependent on the appropriate selection of its parameters and filtering window size. Fixed or manually selected parameters may not provide satisfactory performance across images characterized by different noise distributions and intensities. To overcome this limitation, this study proposes a modified and adaptive bilateral filtering framework in which the filter parameters and weighting coefficients are automatically optimized using the Cuckoo Optimization Algorithm (COA). The proposed hybrid approach exploits the global optimization capability of COA, inspired by the parasitic breeding behavior of cuckoos, to efficiently search the parameter space and identify an appropriate combination of bilateral-filter coefficients. An objective fitness function based on image-quality measures is employed to guide the optimization process toward improved denoising performance. The proposed framework is evaluated experimentally on benchmark medical image datasets and compared with conventional and alternative filtering approaches. Both quantitative and qualitative assessments demonstrate that the proposed method provides superior noise suppression while maintaining important anatomical structures, fine textures, and sharp image boundaries. In particular, the method exhibits robust performance under high-density noise conditions, where excessive smoothing can substantially degrade clinically relevant information. The results demonstrate the effectiveness of integrating metaheuristic optimization with edge-preserving filtering and indicate that the proposed framework can improve the quality and interpretability of medical images for subsequent computer-aided analysis and clinical applications.
Keywords
Medical Image Denoising
Bilateral Filter
Cuckoo Optimization Algorithm (COA)
Parameter Optimization
Edge Preservation
Image Quality Enhancement
[1]      Meng, W., Huisheng, Z., & He, H. (2008). A pseudo cross bilateral filter for image denoising based on Laplacian pyramid. In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (pp. 235–238). IEEE. https://doi.org/10.1109/KAMW.2008.4810469
[2]      Qinlan, X., & Hong, C. (2009). Image denoising based on adaptive filtering and multi-frame averaging filtering. In 2009 International Conference on Artificial Intelligence and Computational Intelligence (pp. 523–526). IEEE. https://doi.org/10.1109/AICI.2009.490
[3]      Liao, Z., Hu, S., Yu, Z., & Sun, D. (2010). Medical image blind denoising using context bilateral filter. In 2010 International Conference of Medical Image Analysis and Clinical Application (pp. 12–17). IEEE. https://doi.org/10.1109/MIACA.2010.5528280
[4]      Patil, P. D., & Kumbhar, A. D. (2015). Bilateral filter for image denoising. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 299–302). IEEE. https://doi.org/10.1109/ICGCIoT.2015.7380477
[5]      Mustafa, Z. A., & Kadah, Y. M. (2011). Multi resolution bilateral filter for MR image denoising. In 2011 1st Middle East Conference on Biomedical Engineering (pp. 180–184). IEEE. https://doi.org/10.1109/MECBME.2011.5752095
[6]      Bibina, V. C., & Viswasom, S. (2012). Adaptive wavelet thresholding & joint bilateral filtering for image denoising. In 2012 Annual IEEE India Conference (INDICON) (pp. 1100–1104). IEEE. https://doi.org/10.1109/INDCON.2012.6420781
[7]      Jith, O. U. N., & Babu, R. V. (2014). Joint bilateral filtering based non-local means image denoising. In 2014 International Conference on Signal Processing and Communications (SPCOM) (pp. 1–5). IEEE. https://doi.org/10.1109/SPCOM.2014.6984014
[8]      Olfa, M., & Nawres, K. (2014). Ultrasound image denoising using a combination of bilateral filtering and stationary wavelet transform. In International Image Processing, Applications and Systems Conference (pp. 1–5). IEEE. https://doi.org/10.1109/IPAS.2014.7043258
[9]      Yu, H., Zhao, L., & Wang, H. (2009). Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain. IEEE Transactions on Image Processing, 18(10), 2364–2369. https://doi.org/10.1109/TIP.2009.2026685
[10]   De Decker, A., Lee, J. A., & Verlysen, M. (2009). Variance stabilizing transformations in patch-based bilateral filters for Poisson noise image denoising. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3673–3676). IEEE. https://doi.org/10.1109/IEMBS.2009.5334715
[11]   Thakur, K., Damodare, O., & Sapkal, A. (2015). Hybrid method for medical image denoising using Shearlet transform and bilateral filter. In 2015 International Conference on Information Processing (ICIP) (pp. 220–224). IEEE. https://doi.org/10.1109/INFOP.2015.7489382
[12]   Haddadnia, J., Rahmani-Seryasat, O., Ghayoumi-Zadeh, H., & Rabiee, H. (2015). An efficient method for detection of masses in mammogram images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 2269–2277.
[13]   Shakeri, N., & Rahmani Seryasat, O. (2025). Improving medical image segmentation using a hybrid ResUNet-Transformer architecture for liver tumor detection. Transactions on Machine Intelligence, 8(1), 57–68. https://doi.org/10.47176/TMI.2025.57
[14]   Zamani, M., Azadi, S., & Rahmani Seryasat, O. (2021). Noise reduction in medical X-ray images using wavelet and neural networks. Transactions on Machine Intelligence, 4(1), 36–52. https://doi.org/10.47176/TMI.2021.36
[15]   Kaur, A., Singh, H., & Arora, D. (2017). An efficient approach for image denoising based on edge-aware bilateral filter. In 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 56–61). IEEE. https://doi.org/10.1109/ISPCC.2017.8269649
[16]   Chaudhury, K. N., & Rithwik, K. (2015). Image denoising using optimally weighted bilateral filters: A SURE and fast approach. In 2015 IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/ICIP.2015.7350769
[17]   Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11(8), 5508–5518. https://doi.org/10.1016/j.asoc.2011.05.008
[18]   Cocosco, C. A. (1997). BrainWeb: Online interface to a 3D MRI simulated brain database. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University.
Transactions on Machine Intelligence
Volume 8, Issue 2
Spring 2025
Pages 128-137

  • Receive Date 04 February 2025
  • Revise Date 26 April 2025
  • Accept Date 05 June 2025