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Multimodal Medical Image Fusion using Rolling Guidance Filter with CNN and Nuclear Norm Minimization


Shuaiqi Liu*, Lu Yin, Siyu Miao, Jian Ma, Shuai Cong and Shaohai Hu   Pages 1 - 16 ( 16 )


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; rolling guidance filter; nuclear norm minimization; shared similarity patches; convolutional neural network; deep learning


College of Electronic and Information Engineering, Hebei University, Baoding Hebei,, Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding Hebei,, Machine Vision Engineering Research Center of Hebei Province, Baoding Hebei,, Machine Vision Engineering Research Center of Hebei Province, Baoding Hebei,, Industrial and Commercial College, Hebei University, Baoding Hebei,, College of Computer and Information, Beijing Jiaotong University, Beijing

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