Multimodal Medical Image Fusion Using Hybrid Layer Decomposition With CNN-Based Feature Mapping and Structural Clustering

2020 ◽  
Vol 69 (6) ◽  
pp. 3855-3865 ◽  
Author(s):  
Sneha Singh ◽  
R. S. Anand
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Feature Mapping ◽  
Hybrid Layer ◽  
Medical Image Fusion ◽  
Structural Clustering

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

2020 ◽  
Vol 16 ◽  
Author(s):  
Shuaiqi Liu ◽  
Lu Yin ◽  
Siyu Miao ◽  
Jian Ma ◽  
Shuai Cong ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Nuclear Norm ◽  
Base Layer ◽  
Feature Mapping ◽  
Fusion Algorithm ◽  
Nuclear Norm Minimization ◽  
Medical Image Fusion ◽  
Norm Minimization ◽  
Fused Image

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.

scholarly journals Medical Image Fusion Based on Transformation Domain Approaches

2020 ◽  
Vol 981 ◽  
pp. 042082
Author(s):  
Raja Krishnamoorthi ◽  
Annapurna Bai ◽  
A. Srinivas
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion

scholarly journals An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

2017 ◽  
Vol 9 (4) ◽  
pp. 61 ◽  
Author(s):  
Guanqiu Qi ◽  
Jinchuan Wang ◽  
Qiong Zhang ◽  
Fancheng Zeng ◽  
Zhiqin Zhu
Keyword(s):  
Image Fusion ◽  
Dictionary Learning ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Fusion Framework

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

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