ReviewBMC medical imaging2026
A comprehensive survey of multi-modality medical image fusion: methods, challenges, and applications.
Review in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Medical image fusion (MIF) is the process of fusing two medical pictures from different modalities into one image. This technology tries to produce a fused output image from two source images that contain more effective and relevant information. This image is used in the healthcare industry, specifically for disease diagnosis. The main challenge is using a single image modality to diagnose diseases accurately. The fused image includes spectral and structural information for the source images to help doctors with disease diagnosis problems. Positron emission tomography (PET), magnetic resonance imaging (MRI), computed tomography (CT), and single photon emission computed tomography (SPECT) are some of the medical imaging modalities. Each modality has its benefits and drawbacks. Researchers have presented different MIF techniques that obtain high fusion results in the MIF field. This paper is a comprehensive survey of multiple state-of-the-art MIF techniques in the spatial and transform domains. It also discusses the main MIF evaluation metrics. Finally, quantitative and qualitative evaluations for some of these techniques are obtained.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.