Assistant Professor, Department of Electrical and Biomedical Engineering, Shomal University
Abstract
Medical images acquired through modalities such as MRI, CT, and ultrasound are inherently corrupted by various types of noise during acquisition and transmission, which severely complicates accurate clinical diagnosis and quantitative interpretation. The bilateral filter has emerged as a widely accepted and powerful framework for edge-preserving noise reduction due to its unique capability to simultaneously utilize both spatial domain and intensity range information. However, dynamically determining its optimal parameters and window sizes for different noise levels remains a persistent challenge in image processing. To address this limitation, this study introduces a robust, modified bilateral filter whose weights and parameters are automatically and intelligently optimized using the Cuckoo Optimization Algorithm (COA). Utilizing an adaptive global search mechanism inspired by the parasitic brooding behavior of cuckoos, the proposed algorithm successfully identifies the optimal combination of filter coefficients by maximizing an objective fitness function based on image quality metrics. Experimental evaluations on benchmark medical datasets demonstrate that the proposed hybrid method significantly outperforms conventional filtering techniques. The quantitative and qualitative results indicate that this approach not only effectively suppresses noise under high-density scenarios but also remarkably preserves critical structural details, fine textures, and sharp boundaries, thereby enhancing the diagnostic value of the clinical images.
