scholarly journals Multimodal medical image fusion using Butterworth high pass filter and Cross bilateral filter

2016 ◽  
Vol 57 ◽  
pp. 01021
Author(s):  
Bharti Lalotra ◽  
Renu Vig ◽  
Sumit Budhiraja
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Bilateral Filter ◽  
Pass Filter ◽  
Medical Image Fusion ◽  
High Pass Filter ◽  
Cross Bilateral Filter ◽  
High Pass

Multi-modality medical image fusion using cross-bilateral filter and neuro-fuzzy approach

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Harmeet Kaur ◽  
Satish Kumar ◽  
KuljinderSingh Behgal ◽  
Yagiyadeep Sharma
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Bilateral Filter ◽  
Fuzzy Approach ◽  
Medical Image Fusion ◽  
Neuro Fuzzy ◽  
Cross Bilateral Filter

Medical Image Fusion Based on SWT and Cross Bilateral Filter

2021 ◽  
pp. 893-899
Author(s):  
Shaik Gowhar ◽  
Maruturi Haribabu ◽  
Velmathi Guruviah
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Bilateral Filter ◽  
Medical Image Fusion ◽  
Cross Bilateral Filter

Optimizing the High-Pass Filter Addition Technique for Image Fusion

2008 ◽  
Vol 74 (9) ◽  
pp. 1107-1118 ◽  
Author(s):  
Ute G. Gangkofner ◽  
Pushkar S. Pradhan ◽  
Derrold W. Holcomb
Keyword(s):  
Image Fusion ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass

New temporal high-pass filter nonuniformity correction based on bilateral filter

2011 ◽  
Vol 18 (2) ◽  
pp. 197-202 ◽  
Author(s):  
Chao Zuo ◽  
Qian Chen ◽  
Guohua Gu ◽  
Weixian Qian
Keyword(s):  
Bilateral Filter ◽  
Nonuniformity Correction ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass

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.

Optimizing the High-Pass Filter Addition Technique for Image Fusion

2007 ◽  
Vol 73 (9) ◽  
pp. 1107-1118 ◽  
Author(s):  
Ute G. Gangkofner ◽  
Pushkar S. Pradhan ◽  
Derrold W. Holcomb
Keyword(s):  
Image Fusion ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass
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