NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network-Reference-Cited by-同舟云学术

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NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

Published:2020-12 Issue:16 Volume:14 Page:4291-4305
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Das Manisha¹^ORCID,Gupta Deep¹^ORCID,Radeva Petia²,Bakde Ashwini M.³

Affiliation:

1. Department of Electronics and Communication EngineeringVisvesvaraya National Institute of Technology NagpurIndia

2. Department of Mathematics and Computer ScienceUniversity of BarcelonaSpain

3. Department of Radio‐DiagnosisAll India Institute of Medical Sciences NagpurIndia

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2020.0219

Reference46 articles.

1. Medical image fusion: A survey of the state of the art

2. Pixel-level image fusion: A survey of the state of the art

3. From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications

4. Review of pulse-coupled neural networks

5. Review of image fusion based on pulse‐coupled neural network;Wang Z.;Arch. Comput. Methods Eng.,2016

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1. Deep and Bio-Inspired Spiking Neural Networks Based Optimized Multi-modal Neurological Image Fusion Model;Lecture Notes in Computer Science;2024

2. A Systematic Literature Review on Multimodal Medical Image Fusion;Multimedia Tools and Applications;2023-07-19

3. A Novel Fusion Strategy and Convolutional Sparse Coding for Robot Multisource Image Fusion;Automatic Control and Computer Sciences;2023-04

4. Trusted Network Difference Data Mining Algorithm Based on Artificial Bee Colony Optimization;Journal of Testing and Evaluation;2022-08-09

5. STPGANsFusion: Structure and Texture Preserving Generative Adversarial Networks for Multi-modal Medical Image Fusion;2022 National Conference on Communications (NCC);2022-05-24

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