山东理工大学学报（自然科学版）2024，Vol.38Issue(4)：53-60,8.
基于复剪切波变换与VGG19模型的医学图像融合方法
The medical image fusion method based on the complex shearlet transform and the VGG19 model
摘要
Abstract
To deal with the drawbacks of traditional medical image fusion methods,such as the insufficient clarity of fine details,the loss of edge information,and the image distortion,as well as the insufficient training datasets for deep learning,a multi-modal medical image fusion method based on the complex shearlet transform(CST)and the pre-trained VGG19 network model is proposed.The CST is firstly em-ployed to extract the edge and texture information,which are represented by the multi-scale and multi-di-rectional sub-band coefficients.Then,the low-frequency sub-band coefficients are fused using the weigh-ted local energy and a modified Laplacian operator.The pre-trained VGG19 model is introduced to extract the multi-layer feature maps,which are combined with the weighted evaluation rules to obtain the fused the high-frequency sub-bands.Finally,the fused high-frequency and low-frequency sub-bands are treated by the inverse transform of the CST to reconstruct the fused image.Experimental results demonstrate that the proposed method produces the fused images that not only display clear details and contours but also effectively suppress the generation of artifacts and distortion.Better fusion performance can be achieved by the proposed method via subjective visual comparison and six objective evaluation metrics.关键词
医学图像/图像融合/复剪切波变换/VGG19 模型/修正的拉普拉斯算子Key words
medical image/image fusion/complex shearlet transform/VGG19 model/modified Laplacian op-erator分类
信息技术与安全科学引用本文复制引用
王钰帏,王雷,郭新萍,程天琪..基于复剪切波变换与VGG19模型的医学图像融合方法[J].山东理工大学学报（自然科学版）,2024,38(4):53-60,8.基金项目
山东省自然科学基金项目(ZR2021MF017) （ZR2021MF017）
