Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB) Article Swipe
Luis C. García-Peraza-Herrera
,
Conor C. Horgan
,
Sébastien Ourselin
,
Michael Ebner
,
Tom Vercauteren
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/21681163.2022.2160377
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/21681163.2022.2160377
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.
Related Topics
Concepts
Hyperspectral imaging
Artificial intelligence
Computer science
RGB color model
Computer vision
Snapshot (computer storage)
Pixel
Segmentation
Spectral imaging
Context (archaeology)
Image segmentation
Image resolution
Spatial contextual awareness
Medical imaging
Remote sensing
Geography
Database
Archaeology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/21681163.2022.2160377
- OA Status
- hybrid
- Cited By
- 5
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317796999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4317796999Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/21681163.2022.2160377Digital Object Identifier
- Title
-
Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-23Full publication date if available
- Authors
-
Luis C. García-Peraza-Herrera, Conor C. Horgan, Sébastien Ourselin, Michael Ebner, Tom VercauterenList of authors in order
- Landing page
-
https://doi.org/10.1080/21681163.2022.2160377Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1080/21681163.2022.2160377Direct OA link when available
- Concepts
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Hyperspectral imaging, Artificial intelligence, Computer science, RGB color model, Computer vision, Snapshot (computer storage), Pixel, Segmentation, Spectral imaging, Context (archaeology), Image segmentation, Image resolution, Spatial contextual awareness, Medical imaging, Remote sensing, Geography, Database, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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