Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

2019 ◽  
Vol 68 (1) ◽  
pp. 49-64 ◽  
Author(s):  
Ming Yin ◽  
Xiaoning Liu ◽  
Yu Liu ◽  
Xun Chen
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Transform Domain ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Pulse Coupled Neural Network ◽  
Medical Image Fusion ◽  
Parameter Adaptive

scholarly journals Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jingming Xia ◽  
Yi Lu ◽  
Ling Tan
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Pulse Coupled Neural Network ◽  
Medical Image Fusion ◽  
Imaging Characteristics ◽  
Parameter Adaptive

Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion results due to the loss of detailed feature information during fusion. To deal with it, this paper proposes a new multimodal medical image fusion method based on the imaging characteristics of medical images. In the proposed method, the non-subsampled shearlet transform (NSST) decomposition is first performed on the source images to obtain high-frequency and low-frequency coefficients. The high-frequency coefficients are fused by a parameter‐adaptive pulse-coupled neural network (PAPCNN) model. The method is based on parameter adaptive and optimized connection strength β adopted to promote the performance. The low-frequency coefficients are merged by the convolutional sparse representation (CSR) model. The experimental results show that the proposed method solves the problems of difficult parameter setting and poor detail preservation of sparse representation during image fusion in traditional PCNN algorithms, and it has significant advantages in visual effect and objective indices compared with the existing mainstream fusion algorithms.

A Novel Medical Image Fusion Approach Based on Nonsubsampled Shearlet Transform

2019 ◽  
Vol 9 (9) ◽  
pp. 1815-1826 ◽  
Author(s):  
Liangliang Li ◽  
Linli Wang ◽  
Zuoxu Wang ◽  
Zhenhong Jia ◽  
Yujuan Si ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Medical Image Fusion ◽  
Fusion Approach
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