FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications Article Swipe
Anna‐Maria Raita‐Hakola
,
Leevi Annala
,
Vivian Lindholm
,
Roberts Trops
,
Antti Näsilä
,
Heikki Saari
,
Annamari Ranki
,
Ilkka Pölönen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22093420
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22093420
Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22093420
- https://www.mdpi.com/1424-8220/22/9/3420/pdf?version=1651732241
- OA Status
- gold
- Cited By
- 8
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225279876
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225279876Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22093420Digital Object Identifier
- Title
-
FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and ApplicationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-29Full publication date if available
- Authors
-
Anna‐Maria Raita‐Hakola, Leevi Annala, Vivian Lindholm, Roberts Trops, Antti Näsilä, Heikki Saari, Annamari Ranki, Ilkka PölönenList of authors in order
- Landing page
-
https://doi.org/10.3390/s22093420Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/9/3420/pdf?version=1651732241Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/9/3420/pdf?version=1651732241Direct OA link when available
- Concepts
-
Hyperspectral imaging, Computer science, Convolutional neural network, Spectral imaging, Multispectral image, Artificial intelligence, Remote sensing, Pixel, Imaging spectroscopy, Computer vision, Medical imaging, Optics, Physics, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.simultaneously | 164 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5042073536 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I94722563 |
| citation_normalized_percentile.value | 0.640084 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |