Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications
作者机构：Department of ECE and UCRDChandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of ECEUttaranchal Institute of TechnologyUttaranchal UniversityDehradun248007India IES College of TechnologyIES UniversityBhopal462044India College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq Department of Electrical EngineeringUniversity of North FloridaJacksonvilleFL32224USA
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第78卷第3期
页 面：4317-4342页
核心收录：
学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
主 题：Image fusion fractal data analysis biomedical diseases research multiresolution analysis numerical analysis
摘 要：Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical *** fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many ***,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing *** solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion ***,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing *** outputs of CBF are then subtracted from the original images to obtain detailed *** further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural *** regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate *** outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the *** addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical *** standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion *** results from th
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