Abstract
Multimodal medical image fusion is crucial for enhancing diagnostic accuracy by integrating complementary information from different imaging modalities. Current fusion techniques face challenges in effectively combining heterogeneous features while preserving critical diagnostic information. This paper presents a Temporal Decomposition Network (TDN), a novel deep learning architecture that optimizes multimodal medical image fusion through feature-level temporal analysis and adversarial learning mechanisms. The TDN architecture incorporates two key components: a salient perception model for discriminative feature extraction and a generative adversarial network for temporal feature matching. The salient perception model identifies and classifies distinct pixel distributions across different imaging modalities, while the adversarial component facilitates accurate feature mapping and fusion. This approach enables precise temporal Decomposition of heterogeneous features and robust quality assessment of fused regions. Experimental validation on diverse medical image datasets, encompassing multiple modalities and image dimensions, demonstrates the TDN's superior performance. Compared to state-of-the-art methods, the framework achieves an 11.378% improvement in fusion accuracy and a 12.441% enhancement in precision. These results indicate significant potential for clinical applications, particularly in radiological diagnosis, surgical planning, and medical image analysis, where multimodal visualization is critical for accurate interpretation and decision-making.