School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China.
J Xray Sci Technol. 2020;28(5):1001-1016. doi: 10.3233/XST-200684.
Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning.
Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method.
The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem.
Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment.
多模态医学图像融合在现代医学的诊断和治疗计划等许多领域都起着至关重要的作用。
由于结构张量具有保持图像几何形状的特性，我们利用它来构建方向结构张量，并进一步提出了一种改进的 3D 医学图像融合方法。
局部熵度量用于构建不同源图像的梯度权重，传统结构张量的特征向量与图像的二阶导数相结合，构建方向结构张量。此外，采用导向滤波获取源图像的细节分量，并构建增强细节的融合梯度场。最后，通过求解泛函最小化问题生成融合图像。
实验结果表明，该方法在视觉效果和定量评估方面均优于传统结构张量和多尺度分析。
