Shearlet Based Medical Image Fusion Using Pulse-Coupled Neural Network with Fuzzy Memberships

2017 ◽  
pp. 337-344
Author(s):  
Niladri Shekhar Mishra ◽  
Sudeb Das ◽  
Amlan Chakrabarti
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Pulse Coupled Neural Network ◽  
Medical Image Fusion

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.

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.

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