Multimodal Medical Image Fusion in Spatial‐Transformed Domains

Fusion of multimodal medical images has become an essential reference for clinical diagnosis and a prominent topic in information fusion research. However, balancing the fusion performance with computational efficiency remains a challenging issue. In this paper, we propose an effective and efficient hybrid fusion method for multimodal medical images that integrates the merits of both spatial and transform domains, rather than relying on a single approach. First, a highly efficient multi‐resolution tool, the framelet transform (FT), is employed to decompose medical images from different modalities into a series of low‐ and high‐frequency sub‐images. Next, an improved version of the structure tensor is designed to fuse the low‐frequency sub‐images, while a modified side‐window filter (SWF) model is applied to fuse the high‐frequency sub‐images. Finally, the inverse FT is used to reconstruct the final fused image. Extensive simulation experiments on more than 120 pairs of multimodal medical images validate the performance of the proposed method, with results demonstrating superior visual quality and objective evaluation metrics compared to several recently published state‐of‐the‐art approaches.