Multimodal medical image registration and fusion integrate complementary features from different modalities, to enhance diagnostic accuracy and provide comprehensive clinical insights. Existing approaches face critical shortcomings in feature alignment, computational efficiency and clinical interpretability, demanding a novel coupled framework to address these issues. Additionally, the lack of open-source benchmark datasets at the systemic level persists as a major bottleneck. Thus, a novel Imaging Coupled Filtering (ICF), means multi-channel image features coupling filtering, is proposed in this work. First, ICF decomposes source images from different modalities into four feature channels: smoothing, texture, contour and edge. Then, intra-channel fusion strategies are designed to generate fused images. Specifically, in the smoothing channels, we propose a visual saliency decomposition strategy to comprehensively extract energy and partial fiber texture features through multi-scale and multi-dimensional analysis, thereby optimizing the utilization of latent feature information. For the texture channels, we propose a novel texture enhancement operator designed to effectively capture fine details and hierarchical structural information, which enables accurate differentiation of invasion states in adherent lesions. Finally, an imaging coupling mechanism is presented to achieve fused results based on the weights of multi-feature representation. Additionally, we have registered and released 403 groups of multimodal abdominal medical images (Ab-MI) for research purposes. Experiments on Atlas and Ab-MI demonstrate that, compared to six state-of-the-art methods, ICF achieves superior results in terms of visual effects, objective metrics and computational efficiency.
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