基于双残差超密集网络的多模态医学图像融合

针对基于残差网络和密集网络的图像融合方法存在网络中间层的部分有用信息丢失和融合图像细节不清晰的问题,提出了基于双残差超密集网络(Dual Residual Hyper-Densely Networks,DRHDNs)的多模态医学图像融合方法。DRHDNs分为特征提取和特征融合两部分。特征提取部分通过将超密集连接与残差学习相结合,构造出双残差超密集块,用于提取特征,其中超密集连接不仅发生在同一路径的层之间,还发生在不同路径的层之间,这种连接使特征提取更充分,细节信息更丰富,并且对源图像进行了初步的特征融合。特征融合部分则进行最终的融合。通过实验将其与另外6种图像融合方法对4组脑部图像进行了融合比较,并根据4种评价指标进行了客观比较。结果显示,DRHDNs在保留细节、对比度和清晰度等方面都有很好的表现,其融合图像细节信息丰富并且清晰,便于疾病的诊断。

Multimodal Medical Image Fusion Based on Dual Residual Hyper Densely Networks

The image fusion method based on residual network and dense network has the problem of losing some useful information in the middle layer of network and unclear details of fusion image.Therefore,a multi-modal medical image fusion based on the Dual Residual Hyper-Densely Networks(DRHDNs)is proposed.DRHDNs is divided into two parts:feature extraction and feature fusion.In the feature extraction part,a dual residual hyper dense blocks is constructed by combining hyper dense connection and residual learning.The hyper dense connection not only occurs between layers in the same path,but also between layers in different paths.This connection makes the feature more sufficient,the detail information more abundant,and the initial feature fusion of the source image is carried out.Feature fusion part is for final fusion.Compared with the other six image fusion methods,four groups of brain images are fused and compared,and an objective comparison is made from four evaluation indexes.Results show that DRHDNs has good performance in detail retention and contrast.The fusion image has rich and clear detail information,which conforms to human visual.