Multimodal Medical Image Fusion: Techniques, Databases, Evaluation Metrics, and Clinical Applications -A Comprehensive Review

Multi-modal Medical Image Fusion (MMIF) is an advancing field at the intersection of medical imaging, data science, and clinical diagnostics. It aims to integrate complementary data from various imaging modalities, such as MRI, CT, and PET, into a single, diagnostically superior composite image. The limitations of unimodal imaging, such as low spatial resolution, insufficient contrast, or incomplete functional characterization, have catalyzed the development of MMIF techniques to enable enhanced visualization, precise diagnosis, and personalized therapeutic planning. This review provides a comprehensive synthesis of the MMIF landscape, categorizing methodologies into five principal domains such as spatial, frequency-based, sparse representation, deep learning, and hybrid approaches. Each technique is critically evaluated for its advantages, limitations, and applicability in clinical settings. Preprocessing, registration, fusion execution, and validation are covered in this review, along with levels of fusion pixel, feature, and decision. The study reviews prominent public databases, including TCIA, OASIS, ADNI, MIDAS, AANLIB, and DDSM, comparing their imaging modalities, disease coverage, file formats, and accessibility. The evaluation of MMIF techniques is systematically addressed, providing a framework for objective performance assessment. An experimental setup is implemented on two datasets to assess the comparative efficacy of selected MMIF techniques utilizing quantitative evaluation variables such as SSIM, entropy, spatial frequency, and mutual information. The results highlight the effectiveness of hybrid and deep learning-based approaches in maintaining both anatomical detail and functional consistency across modalities. The review explores MMIF’s real-world clinical applications, including image-guided neurosurgery, spinal planning, stereotactic radiosurgery, orthopedic implant design, and oncology diagnostics. It also provides insights into future directions, such as explainable AI, federated learning, and integration with robotic surgeries. MMIF offers immense potential yet has limitations like registration errors, computational burdens, generation of artifacts, loss of specific information, and a lack of standardized evaluation metrics. Essentially, the study provides an analytical basis for healthcare experts, scientists, and engineers aiming to develop clinically scalable MMIF systems, which will become indispensable tools for improving diagnostic accuracy, treatment planning, and patient outcomes in modern healthcare.