A Multi-module Medical Image Fusion Method Based on Non-subsampled Shear Wave Transformation and Convolutional Neural Network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mingju Zhao ◽  
Yuping Peng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Shear Wave ◽  
Medical Image ◽  
Wave Transformation ◽  
Fusion Method ◽  
Medical Image Fusion ◽  
Image Fusion Method

scholarly journals A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lei Wang ◽  
Chunhong Chang ◽  
Zhouqi Liu ◽  
Jin Huang ◽  
Cong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Deep Convolutional Neural Network ◽  
Fusion Method ◽  
Medical Image Fusion ◽  
Low Pass ◽  
High Pass ◽  
Image Fusion Method

The traditional medical image fusion methods, such as the famous multi-scale decomposition-based methods, usually suffer from the bad sparse representations of the salient features and the low ability of the fusion rules to transfer the captured feature information. In order to deal with this problem, a medical image fusion method based on the scale invariant feature transformation (SIFT) descriptor and the deep convolutional neural network (CNN) in the shift-invariant shearlet transform (SIST) domain is proposed. Firstly, the images to be fused are decomposed into the high-pass and the low-pass coefficients. Then, the fusion of the high-pass components is implemented under the rule based on the pre-trained CNN model, which mainly consists of four steps: feature detection, initial segmentation, consistency verification, and the final fusion; the fusion of the low-pass subbands is based on the matching degree computed by the SIFT descriptor to capture the features of the low frequency components. Finally, the fusion results are obtained by inversion of the SIST. Taking the typical standard deviation, QAB/F, entropy, and mutual information as the objective measurements, the experimental results demonstrate that the detailed information without artifacts and distortions can be well preserved by the proposed method, and better quantitative performance can be also obtained.

A Nonsubsampled Contourlet Transform Based Medical Image Fusion Method

2013 ◽  
Vol 12 (4) ◽  
pp. 749-755 ◽  
Author(s):  
Shen Yu ◽  
Ren Enen ◽  
Dang Jian-Wu ◽  
Wang Guo-Hua ◽  
Feng Xin
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Image Fusion Method

Multi-focus image fusion method based on two stage of convolutional neural network

2020 ◽  
Vol 176 ◽  
pp. 107681
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen ◽  
Pengxiang Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Fusion Method ◽  
Two Stage ◽  
Focus Image ◽  
Image Fusion Method

Medical image fusion method by using Laplacian pyramid and convolutional sparse representation

2019 ◽  
Vol 32 (17) ◽  
Author(s):  
Feiqiang Liu ◽  
Lihui Chen ◽  
Lu Lu ◽  
Awais Ahmad ◽  
Gwanggil Jeon ◽  
...  
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Medical Image ◽  
Fusion Method ◽  
Laplacian Pyramid ◽  
Medical Image Fusion ◽  
Image Fusion Method

Medical image fusion method based on guided filter

2019 ◽  
Vol 34 (6) ◽  
pp. 605-612
Author(s):  
郭 盼 GUO Pan ◽  
何文超 HE Wen-chao ◽  
梁龙凯 LIANG Long-kai ◽  
张 萌 ZHANG Meng ◽  
吕绪浩 LYU Xu-hao ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Guided Filter ◽  
Fusion Method ◽  
Medical Image Fusion ◽  
Image Fusion Method
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