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

scholarly journals Multi-Modal Medical Image Fusion Based on FusionNet in YIQ Color Space

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1423
Author(s):  
Kai Guo ◽  
Xiongfei Li ◽  
Hongrui Zang ◽  
Tiehu Fan
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Information ◽  
Color Space ◽  
Structural Similarity ◽  
Source Image ◽  
Medical Image Fusion ◽  
Structure Recognition ◽  
Fused Image ◽  
Fusion Framework

In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.

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 Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chuangeng Tian ◽  
Lu Tang ◽  
Xiao Li ◽  
Kaili Liu ◽  
Jian Wang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
State Of The Art ◽  
Component Analysis ◽  
Fusion Strategy ◽  
Medical Image Fusion ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Fused Image ◽  
Fusion Framework

This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

A new contrast based multimodal medical image fusion framework

2015 ◽  
Vol 157 ◽  
pp. 143-152 ◽  
Author(s):  
Gaurav Bhatnagar ◽  
Q.M. Jonathan Wu ◽  
Zheng Liu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Fusion Framework
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