A Novel Approach for Enhanced Multimodal Medical Image Fusion

Abstract

The fusion of multimodal medical imaging is crucial in enhancing the clinical utility of medical images for diagnosis and medical issue assessment. Fused images significantly augment the quality of reference images while reducing randomness and redundancy. However, the efficacy of the fused image heavily relies on the chosen fusion techniques. Numerous algorithms have been proposed to enhance the clinical precision of image-based decisions; however, developing efficient fusion methods continues to pose a significant challenge for researchers. This paper proposes a novel multimodal framework for medical image fusion aimed at enhancing output image clarity and diagnosis accuracy. Our approach integrates classical fusion methods such as CV2, DWT, and PCA with multimodal source images. We implemented and evaluated this method in a simulated environment, demonstrating through quantitative analysis that our approach markedly enhances fusion quality compared to existing methods.