Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and provides a basis for clinical diagnosis. Firstly, the low-frequency sub-band and several high-frequency directional sub-bands are obtained by NSST transformation of the source image, and the structural similarity between sub-bands is evaluated. Then, according to the characteristics of low-frequency sub-band images, for sub-images with high similarity, the regional features are obtained by region energy and variance, and the fusion method is based on region feature weighting. For sub-images with low similarity, two images to be fused are input into the LSCN model respectively by fusing the connection items of the improved LSCN model. The improved L-term replaces the ignition frequency in the traditional PCNN as the output. According to the characteristics of high frequency sub-images, the fusion rule of combining visual sensitivity coefficient and regional energy is adopted for sub-images with high similarity. For sub-images with low similarity, an improved guided filter is used to fuse the sub-images in order to maintain the clear edges of the images. Finally, the image is reconstructed by inverse NSST transform. The experimental results show that the proposed algorithm can obtain better fusion effects in both objective and subjective evaluation. The obtained fusion image has rich information, excellent edge retention characteristics, subjectively clear texture and high contrast and good visual effect.

单模态医学图像提供的信息有限，无法反映相关组织的所有细节，这可能导致临床医学中的误诊。因此，提出了一种基于非下采样剪切波变换（NSST）的医学图像融合算法。该算法融合了多模态医学图像，丰富了融合图像的信息，提高了图像质量，为临床诊断提供了依据。首先，通过源图像的NSST变换获得低频子带和几个高频定向子带，并评估子带之间的结构相似性。然后，根据低频子带图像的特征，对于相似度较高的子图像，通过区域能量和方差获得区域特征，融合方法基于区域特征加权。对于低相似度的子图像，通过融合改进的LSCN模型的连接项，分别将两个要融合的图像输入到LSCN模型中。改进的L项取代了传统PCNN中的点火频率作为输出。根据高频子图像的特点，对相似度较高的子图像采用视觉灵敏度系数和区域能量相结合的融合规律。对于具有低相似度的子图像，使用改进的导向滤波器融合子图像，以保持图像的清晰边缘。最后，通过反NSST变换重建图像。实验结果表明，该算法在主观评价和主观评价上均能取得较好的融合效果。