Multimodality medical image fusion algorithm based on gradient minimization smoothing filter and pulse coupled neural network

2016 ◽  
Vol 30 ◽  
pp. 140-148 ◽  
Author(s):  
Xingbin Liu ◽  
Wenbo Mei ◽  
Huiqian Du
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Fusion Algorithm ◽  
Pulse Coupled Neural Network ◽  
Medical Image Fusion ◽  
Smoothing Filter

scholarly journals Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 591
Author(s):  
Liangliang Li ◽  
Hongbing Ma
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Fusion Rule ◽  
Objective Evaluation ◽  
Fusion Model ◽  
Pulse Coupled Neural Network ◽  
Medical Image Fusion ◽  
Frequency Components

Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, QW, API, SD, EN and time consumption.

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