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ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 8 (2026): Volume 10, Issue 8, August 2026 | Pages: 95-99
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-08-2026
Medical image fusion plays an important role in enhancing diagnostic accuracy by combining complementary information from different imaging modalities. Computed Tomography (CT) provides detailed information about bone structures, whereas Magnetic Resonance Imaging (MRI) offers superior soft tissue visualization. This study presents a simple convolutional neural network (CNN)-based feature extraction framework for CT-MRI image fusion. The proposed approach extracts feature from CT and MRI images using a shallow CNN architecture and combines them using Average Fusion and Maximum Fusion rules. Experiments were conducted on 24 CT-MRI image pairs obtained from the Harvard Medical Image Fusion Dataset. The fused images were evaluated using Entropy (EN), Structural Similarity Index Measure (SSIM), and Mutual Information (MI). Experimental results demonstrate that the Maximum Fusion strategy achieved higher entropy and mutual information, while Average Fusion obtained a slightly higher SSIM value. The findings indicate that Maximum Fusion preserves more information from source images and provides better overall fusion performance.
Medical Image Fusion, CT-MRI Fusion, CNN, Maximum Fusion, Average Fusion, Entropy, Mutual Information.
Vikas Jangra, Sumeet Gill, & Archna Kirar. (2026). A Lightweight CNN-Based Approach for CT-MRI Medical Image Fusion Using Average and Maximum Fusion Strategies. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(8), 95-99. Article DOI https://doi.org/10.47001/IRJIET/2026.108010
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