IEEE Trans Image Process. 2017 Dec;26(12):5855-5866. doi: 10.1109/TIP.2017.2745202. Epub 2017 Aug 25.
A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods.
提出了一种新的执行解剖（MRI）-功能（PET 或 SPECT）图像融合的方法。该方法将来自单一或多种医学成像模式的输入图像信号的特定特征信息合并到单个融合图像中，同时保留更多信息并产生更少的失真。所提出的方法使用基于局部拉普拉斯滤波的技术，通过新颖的多尺度系统结构来实现。首先，在多尺度图像表示中生成输入图像，并使用局部拉普拉斯滤波对其进行处理。其次，在每个尺度上，通过局部能量最大化方案将分解的图像组合起来以生成融合的近似图像，并通过基于感兴趣信息的方案生成融合的残差图像。最后，使用类似于传统拉普拉斯金字塔变换的重建过程来获得融合图像。通过主观观察和客观指标计算的单个多尺度分析分解方案或融合规则的实验结果清楚地证明了该方法的优越性。此外，与最先进的融合方法相比，所提出的方法可以获得更好的性能。
