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Cited by 220Advanced Image Fusion TechniquesMedical Image Segmentation TechniquesMultimodal Machine Learning Applications

A review of multimodal medical image fusion techniques

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Huang, Bing, Yang, Feng, Yin, Mengxiao, Mo, Xiaoying, Zhong, Cheng·2020·Computational and Mathematical Methods in Medicine
DOI 10.1155/2020/8279342PMC PMC7195632
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Abstract

The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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Cited by

Sorted by citations

Authors
Huang, B., Yang, F., Yin, M., Mo, X., & Zhong, C.
Affiliations
  • Guangxi University (CN)
  • School of Computer and Electronics and Information, Guangxi University, Nanning, Guangxi 530004, China
ORCID
Venue
Computational and Mathematical Methods in Medicine
Publisher
Wiley
Published
2020-04-23
Volume
2020
Pages
1-16
ISSN
  • 1748-670X (ISSN-L)
  • 1748-6718
DOI
10.1155/2020/8279342
PMC
PMC7195632
OpenAlex
W3020146971
Cited by
220
References
2
Citations by year
2026: 23 · 2025: 39 · 2024: 42 · 2023: 48 · 2022: 39 · 2021: 24 · 2020: 5
FWCI
2.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Natural Science Foundation of Guangxi Province (2017GXNSFAA198269, 2017GXNSFAA198267, 61762007, 61861004)
  • National Natural Science Foundation of China (2017GXNSFAA198269, 2017GXNSFAA198267, 61762007, 61861004)
Topics
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
multimodal medical image fusion, deep learning fusion, imaging modalities, medical image dataset
MeSH terms
Deep Learning, Algorithms, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Tomography, Emission-Computed, Single-Photon, Databases, Factual, Neural Networks, Computer, Computational Biology, Positron-Emission Tomography, Multimodal Imaging

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