Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

2020 ◽  
Vol 83 ◽  
pp. 115758 ◽  
Author(s):  
Qiu Hu ◽  
Shaohai Hu ◽  
Fengzhen Zhang
Keyword(s):  
Image Fusion ◽  
Dictionary Learning ◽  
Medical Image ◽  
Gabor Filtering ◽  
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

A novel dictionary learning approach for multi-modality medical image fusion

2016 ◽  
Vol 214 ◽  
pp. 471-482 ◽  
Author(s):  
Zhiqin Zhu ◽  
Yi Chai ◽  
Hongpeng Yin ◽  
Yanxia Li ◽  
Zhaodong Liu
Keyword(s):  
Image Fusion ◽  
Dictionary Learning ◽  
Medical Image ◽  
Learning Approach ◽  
Medical Image Fusion

scholarly journals A New Dictionary Construction Based Multimodal Medical Image Fusion Framework

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 267 ◽  
Author(s):  
Fuqiang Zhou ◽  
Xiaosong Li ◽  
Mingxuan Zhou ◽  
Yuanze Chen ◽  
Haishu Tan
Keyword(s):  
Image Fusion ◽  
Dictionary Learning ◽  
Medical Image ◽  
Computational Cost ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Computation Efficiency ◽  
Image Patches

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.

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
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