State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science & Engineering, Zhejiang University, Hangzhou, China.
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China.
J Xray Sci Technol. 2023;31(6):1341-1362. doi: 10.3233/XST-230180.
X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. Some methods have been applied in XPCI field, but wavelet-based decomposition approaches often result in loss of original data.
To explore the application value of a novel image fusion method for XPCI system and computed tomography (CT) system.
The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. The effectiveness of other image fusion methods has been compared, such as discrete wavelet transforms and anisotropic diffusion fusion.
The δ-guided FABEMD-MGPCNN method achieved either the first or second position in objective evaluation metrics with biological samples, as compared to other image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT show expected results to retain the feature information.
The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.
X 射线相衬成像（XPCI）可以分离物体的衰减、折射和散射信号。图像融合的应用可以将独特的信息集中到单个图像中。已经有一些方法应用于 XPCI 领域，但基于小波的分解方法通常会导致原始数据丢失。
探索一种新的图像融合方法在 XPCI 系统和计算机断层扫描（CT）系统中的应用价值。
采用快速自适应二维经验模态分解（FABEMD）方法进行图像分解，以避免不必要的信息丢失。提出了一个参数δ，用于指导包含高频信息的二维固有模态函数的融合，使用具有形态梯度（MGPCNN）的脉冲耦合神经网络。残差图像通过能量属性融合策略进行融合。对结果进行图像预处理和增强，以确保其质量。比较了其他图像融合方法的有效性，如离散小波变换和各向异性扩散融合。
与其他图像融合方法相比，在生物样本的客观评价指标中，δ 引导的 FABEMD-MGPCNN 方法要么排名第一，要么排名第二。此外，还与用于 XPCI 的其他融合方法进行了比较。最后，该方法应用于 CT 显示出保留特征信息的预期效果。
与传统和最近的 X 射线差分相位对比成像和计算机断层扫描系统的图像融合方法相比，所提出的 δ 引导的 FABEMD-MGPCNN 方法具有潜在的可行性和优越性。
