Comprehensive Analysis of Techniques and Approaches in Medical Image Fusion

Medical image fusion is a major area in the field of medical imaging, the main objective is to integrate information from multiple imaging modalities to improve diagnostic accuracy. Medical image fusion brings together different types of imaging data—like MRI, CT, and PET scans—to compensate for the weaknesses of using just one method. By merging these complementary views, medical professionals gain a clearer, more complete picture of what’s happening inside the body, which leads to better diagnoses and smarter treatment plans. This article explore the latest techniques for combining these images, from traditional approaches to cutting-edge AI tools. Older methods, such as wavelet transforms and principal component analysis (PCA), focus on preserving fine details like edges and textures but often struggle with noisy images or slow processing. Newer AI-driven methods, like generative adversarial networks (GANs) and transformers, excel at learning complex patterns from data, creating sharper fused images. By examining the strengths, limitations, and practical implications of each technique, this paper offers a structured analysis of fusion methods and their applications.