A Novel Medical Image Fusion Method Using Modified Grey Wolf Optimization in Wavelet Transform Domain
Keywords:
Medical image fusion, Enhanced wavelet transform, Modified grey wolfoptimization algorithm, Local energy maxima, Computed tomography.Abstract
Medical image fusion has emerged as a crucial tool in modern healthcare, facilitating comprehensive analysis by integrating different image modalities into a single image. This fused image aids physicians in disease diagnosis and treatment planning. Despite the advancements in fusion methodologies, effectively merging medical images without compromising any information remains a significant challenge, leading to the exploration of novel methodologies. This study introduces a novel approach for image fusion that utilizes the Modified Grey Wolf Optimization (MGWO) algorithm and the Enhanced Wavelet Transform (EWT). Source images are processed using EWT to extract high- and low-frequency subbands. The low-frequency subbands are fused using the Local Energy Maxima (LEM) criterion.High-frequencysubbandsundergo denoisingusingan enhanced thresholding technique, followed by the application of MGWO to determine adaptive weights for integrating high-frequency subbands for a medical image fusion. An inverse wavelet transform reconstructs the fused image from fused low-frequency and high-frequency subbands. Numerous datasets are tested, wherein the quantitative and qualitative evaluation confirms the effectiveness of the proposed method. Compared to standard models, the proposed technique performed well in experiments, highlighting its potential for enhancing medical image fusion and advancing diagnostic capabilities in healthcare applications.
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