Prospects of Structural Similarity Index for Medical Image Analysis
Open accessAbstract
An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.
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- Authors
- Mudeng, V., Kim, M., & Choe, S.-W.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Corresponding author
- Kim, Minseok
- Choe, Se-woon
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-04-08
- Volume
- 12
- Issue
- 8
- Pages
- 3754
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4224251232
- Cited by
- 101
- References
- 135
- Citations by year
- 2026: 22 · 2025: 36 · 2024: 25 · 2023: 17 · 2022: 1
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF-2019R1F1A1062397, NRF- 2020R1I1A3074098)
- the Brain Korea 21 FOUR Project (Dept. of IT Convergence Engineering, Kumoh National Institute of Technology, Dept. of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National In-stitute of Technology)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- structural similarity index, medical image quality assessment, image quality metrics, visual perception, image degradation, medical imaging modalities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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