Background:
Medical image fusion is very important for diagnosis and treatment of disease. In recent years,
there are lots of different multimodal medical image fusion algorithms which can provide delicate contexts for disease
diagnosis more clearly and more convenient. Recently, nuclear norm minimization and deep learning have been used
effectively in image processing.
Method:
A multi-modality medical image fusion method using rolling guidance filter (RGF) with convolutional neural
network (CNN) based feature mapping and nuclear norm minimization (NNM) is proposed. At first, we decompose
medical images to base layer components and detail layer components by using RGF. In the next step, we get the basic
fused image through the pre-trained CNN model. The CNN model with pre-training is used to obtain the significant
characteristics of the base layer components. And we can compute the activity level measurement from the regional
energy of CNN-based fusion maps. Then, a detail fused image is gained by NNM. That is, we use NNM to fuse the detail
layer components. At last, the basic and detail fused images are integrated into the fused result.
Results:
From the comparison with the most advanced fusion algorithms, the results of experiments indicate that this
fusion algorithm has the best effect in visual evaluation and objective standard.
Conclusion:
The fusion algorithm using RGF and CNN-based feature mapping combined with NNM can improve fusion
effects and suppress artifacts and blocking effects in the fused result.
Medical Image Fusion Based on Transformation Domain Approaches
