Abstract
Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A Long-Range Correlation-Guided Dual-Encoder Fusion Network for Medical Images is proposed in this paper. The main innovations of this paper are as follows: Firstly, A Cross-dimension Multi-scale Feature Extraction Module (CMFEM) is designed in the encoder, by extracting multi-scale features and aggregating coarse-to-fine features, the model realizes fine-grained feature enhancement in different modalities. Secondly, a Long-range Correlation Fusion Module (LCFM) is designed, by calculating the long-range correlation coefficient between local features and global features, the same granularity features are fused by the long-range correlation fusion module. long-range dependencies between modalities are captured by the model, and different granularity features are aggregated. Finally, this paper is validated on clinical multimodal lung medical image dataset and brain medical data dataset. On the lung medical image dataset, IE, AG, [Formula: see text], and EI metrics are improved by 4.53%, 4.10%, 6.19%, and 6.62% respectively. On the brain medical image dataset, SF, VIF, and [Formula: see text] metrics are improved by 3.88%, 15.71%, and 7.99% respectively. This model realizes better fusion performance, which plays an important role in the fusion of multimodal medical images.