Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm. Extensive experiments show our proposed network and fusion strategy exceed the state-of-the-art performance compared with reference image fusion methods on four commonly used fusion metrics.
翻译:医学图象在临床应用中起着重要作用。多式医学图象可以为医生诊断病人提供丰富的信息。图像聚合技术能够将多式图象中的补充信息合成为单一图像。这种技术将防止放射学家在不同图象之间互换和互换,在诊断过程中节省大量时间。在本文中,我们为医学图象融合任务引入了一个新型的隔热残留关注网络。我们的网络能够提取多尺度的深层语义特征。此外,我们根据软体轴重量和基质核规范,提出了一个新的固定的聚合战略,称为基于软体轴的加权战略。广泛的实验显示,我们提议的网络和聚变战略超过了最先进的功能,而参照了四种常用集聚度的图象方法。