Multimodal medical image fusion based on mask optimization and parallel attention mechanism

Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases, but existing fusion methods have problems such as blurred texture details, low contrast, and inability to fully extract fused image information. Therefore, a multimodal medical image fusion method based on mask optimization and parallel attention mechanism was proposed to address the aforementioned issues. Firstly, it converted the entire image into a binary mask, and constructed a contour feature map to maximize the contour feature information of the image and a triple path network for image texture detail feature extraction and optimization. Secondly, a contrast enhancement module and a detail preservation module were proposed to enhance the overall brightness and texture details of the image. Afterwards, a parallel attention mechanism was constructed using channel features and spatial feature changes to fuse images and enhance the salient information of the fused images. Finally, a decoupling network composed of residual networks was set up to optimize the information between the fused image and the source image so as to reduce information loss in the fused image. Compared with nine high-level methods proposed in recent years, the seven objective evaluation indicators of our method have improved by 6%-31%, indicating that this method can obtain fusion results with clearer texture details, higher contrast, and smaller pixel differences between the fused image and the source image. It is superior to other comparison algorithms in both subjective and objective indicators.