测试科学与仪器2025，Vol.16Issue(1)：26-36,11.DOI:10.62756/jmsi.1674-8042.2025003
基于掩膜优化和并联注意力机制的多模态医学图像融合
Multimodal medical image fusion based on mask optimization and parallel attention mechanism
摘要
Abstract
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.关键词
多模态医学图像融合/二值掩膜/对比度增强模块/并联注意力机制/解耦网络Key words
multimodal medical image fusion/binary mask/contrast enhancement module/parallel attention mechanism/decoupling network引用本文复制引用
邸敬,梁婵,郭文庆,廉敬..基于掩膜优化和并联注意力机制的多模态医学图像融合[J].测试科学与仪器,2025,16(1):26-36,11.基金项目
This work was supported by Gansu Natural Science Foundation Programme(No.24JRRA231),National Natural Science Foundation of China(No.62061023),and Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04). （No.24JRRA231）
