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Article

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

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Author to whom correspondence should be addressed.
Academic Editors: Jiayi Ma, Yu Liu, Junjun Jiang, Zheng Wang and Han Xu
Entropy 2021, 23(5), 591; https://doi.org/10.3390/e23050591
Received: 22 March 2021 / Revised: 26 April 2021 / Accepted: 30 April 2021 / Published: 11 May 2021
(This article belongs to the Special Issue Advances in Image Fusion)
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. View Full-Text
Keywords: multimodal medical image; image fusion; PCNN; WSEML; GIF; NSCT multimodal medical image; image fusion; PCNN; WSEML; GIF; NSCT
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MDPI and ACS Style

Li, L.; Ma, H. Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain. Entropy 2021, 23, 591. https://doi.org/10.3390/e23050591

AMA Style

Li L, Ma H. Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain. Entropy. 2021; 23(5):591. https://doi.org/10.3390/e23050591

Chicago/Turabian Style

Li, Liangliang, and Hongbing Ma. 2021. "Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain" Entropy 23, no. 5: 591. https://doi.org/10.3390/e23050591

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