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

基于掩膜优化和并联注意力机制的多模态医学图像融合

作　　者：DI Jing LIANG Chan GUO Wenqing LIAN Jing 邸敬;梁婵;郭文庆;廉敬(兰州交通大学电子与信息工程学院,甘肃兰州730070)

机构地区：[1]School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China

出　　处：《Journal of Measurement Science and Instrumentation》2025年第1期26-36,共11页测试科学与仪器(英文版)

基　　金：supported by Gansu Natural Science Foundation Programme(No.24JRRA231);National Natural Science Foundation of China(No.62061023);Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。

摘　　要：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.医学图像融合技术对于提高疾病的检测精确度和治疗效率至关重要,但现有的融合方法存在纹理细节模糊、对比度低,无法充分提取融合图像信息等问题。为此,提出了一种掩膜优化和级联式注意力机制的多模态医学图像融合方法。首先,将整幅图像转换为二值掩膜,构建轮廓特征图最大程度提升图像轮廓特征信息,并构建三重路径网络进行图像纹理细节特征提取和优化。其次,提出一种图像对比度增强模块和细节保留模块提升图像整体亮度和纹理细节。然后,利用通道特征和空间特征变化构造了一种并联注意力机制来融合图像,提升融合图像的显著信息。最后,设置了一个由残差网络构成的解耦网络,将融合图像与源图像之间的信息进行优化,旨在减少融合图像信息丢失。通过与近年来提出的9种高水平方法相比,本文方法的7项客观评价指标有6%-31%的提升,说明本文方法能够获得纹理细节更清晰、对比度更高和融合图像与源图像像素差异较小的融合结果,在主观和客观指标上都优于其他对比算法。

关 键 词：multimodal medical image fusion binary mask contrast enhancement module parallel attention mechanism decoupling network 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]