Multimodal Image Fusion for Enhanced Medical Diagnosis

Nowadays, image fusion has developed a dynamic meadow in image processing, specifically in medical analysis. Multimodal Medical Image Fusion (MMIF) improves medical images by merging two or more images of diverse modalities, resulting in a vibrant, and instructive fused image. Deciding on the utmost operative MMIF technique is decisive for attaining greater image eminence. The study delivers an inclusive assessment of MMIF techniques, leading medical imaging modalities, fusion steps, and valuation methodologies. Fundamental modalities consist of Computed Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI). MMIF techniques are classified into various transforms such as spatial domain, fuzzy logic, morphological approaches, and sparse representation methods. Furthermore, fusion may befall on pixel-level, feature-level, or decision-level fusion, through valuation alienated into subjective and objective data. The research, moreover, projects MMIF techniques, emphasizing their corresponding assets and flaws, as well as managerial specialists, concerning ideal imaging resolutions.