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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

General Review Article

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

Author(s): Shuaiqi Liu, Lu Yin, Siyu Miao, Jian Ma, Shuai Cong* and Shaohai Hu

Volume 16, Issue 10, 2020

Page: [1243 - 1258] Pages: 16

DOI: 10.2174/1573405616999200817103920

Price: $65


Background: Medical image fusion is very important for the diagnosis and treatment of diseases. In recent years, there have been a number of different multi-modal medical image fusion algorithms that can provide delicate contexts for disease diagnosis more clearly and more conveniently. Recently, nuclear norm minimization and deep learning have been used effectively in image processing.

Methods: A multi-modality medical image fusion method using a rolling guidance filter (RGF) with a 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 pretrained 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 results.

Keywords: Medical image fusion, rolling guidance filter, nuclear norm minimization, shared similarity patches, convolutional neural network, deep learning.

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