Medical imaging has been an indispensable tool in modern medicine in last decades. Various types of imaging systems provide structural and functional information about tissues. But most of the time both kinds of information are necessary to make proper decision. Image fusion aims at gathering complementary information of different sources in one image to be more informative. This paper proposes a new method for this purpose. In proposed method, source images are first decomposed using nonsubsampled shearlet transform. Extracting most of relevant information and merging them to achieve the best weights for fusion task is done by principal component analysis and particle swarm optimization. Fused image is provided by merging source images according to weights achieved from previous steps. Quantitative and qualitative analysis prove outperformance of our methods compared to well-known fusion methods. The experimental results show improvement compared to subsequent best method, in terms of peak-signal-to-noise-ratio (+ 8.85%), entropy (+ 3.48%), standard deviation (+ 16.3%), and quality index (+ 14.84%).

中文翻译：

---

基于非下采样的小波变换和粒子群算法的多模式医学图像融合

在过去的几十年中，医学成像一直是现代医学中必不可少的工具。各种类型的成像系统提供有关组织的结构和功能信息。但是大多数时候，两种信息都是做出正确决策所必需的。图像融合旨在在一张图像中收集不同来源的互补信息，以提供更多信息。为此，本文提出了一种新方法。在提出的方法中，首先使用非下采样的小波变换对源图像进行分解。通过主成分分析和粒子群优化，可以提取大多数相关信息并将其合并以实现融合任务的最佳权重。通过根据先前步骤获得的权重合并源图像来提供融合图像。与众所周知的融合方法相比，定量和定性分析证明了我们方法的性能。实验结果表明，与随后的最佳方法相比，在峰信噪比（+ 8.85％），熵（+ 3.48％），标准差（+ 16.3％）和质量指数（+ 14.84）方面有所改进％）。