Details
| Original language | English |
|---|---|
| Article number | 096001 |
| Pages (from-to) | 96001 |
| Number of pages | 1 |
| Journal | Journal of biomedical optics |
| Volume | 30 |
| Issue number | 9 |
| Publication status | Published - 1 Sept 2025 |
Abstract
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability. Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach. The obtained hyperfocus images should significantly enhance the diagnostic performance of total body scanners in clinical settings. Approach: We employed focus stacking to merge multiple images, each with a limited depth of field, into a single hyperfocus image, ensuring every part of the skin is in clear focus. The method was implemented in the high-resolution imaging module using an electrically tunable liquid lens to quickly capture a series of differently focused images in vivo. Algorithms were developed for image alignment, focus measurement, and fusion, with the addition of deep learning-based super-resolution techniques to further enhance image quality. A classification model was trained to provide an artificial intelligence (AI)-based lesion classification. Results: The developed optical imaging system successfully produced noncontact dermoscopic images with complete focus across all skin topographies. The hyperfocus images obtained demonstrated high resolution of formula presented and captured focus stacks at 50 frames per second, ensuring rapid acquisition and patient comfort, however, with some variance in resolution of individual lesions compared with contact-based dermoscopy standards. Conclusions: The focus stacking-based approach for noncontact dermoscopy improves the quality of diagnostic images by ensuring an all-in-focus view of differently shaped skin lesions, essential for early melanoma detection. Although the approach marks a substantial improvement in noninvasive skin imaging, the use of super-resolution techniques requires careful consideration to avoid compromising the authenticity of the raw data. This work enables the usage of advanced imaging and AI techniques in total body scanners for early melanoma detection in clinical practice.
Keywords
- artificial intelligence, focus stacking, melanoma diagnostics, noncontact dermoscopy, skin lesion imaging, total body dermoscopy
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Materials Science(all)
- Biomaterials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Biomedical Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Journal of biomedical optics, Vol. 30, No. 9, 096001, 01.09.2025, p. 96001.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - High-resolution imaging system for integration into intelligent noncontact total body scanner
AU - Jütte, Lennart
AU - González-Villà, Sandra
AU - Quintana, Josep
AU - Garcia, Rafael
AU - Roth, Bernhard
N1 - Publisher Copyright: © 2025 The Authors.
PY - 2025/9/1
Y1 - 2025/9/1
N2 - Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability. Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach. The obtained hyperfocus images should significantly enhance the diagnostic performance of total body scanners in clinical settings. Approach: We employed focus stacking to merge multiple images, each with a limited depth of field, into a single hyperfocus image, ensuring every part of the skin is in clear focus. The method was implemented in the high-resolution imaging module using an electrically tunable liquid lens to quickly capture a series of differently focused images in vivo. Algorithms were developed for image alignment, focus measurement, and fusion, with the addition of deep learning-based super-resolution techniques to further enhance image quality. A classification model was trained to provide an artificial intelligence (AI)-based lesion classification. Results: The developed optical imaging system successfully produced noncontact dermoscopic images with complete focus across all skin topographies. The hyperfocus images obtained demonstrated high resolution of formula presented and captured focus stacks at 50 frames per second, ensuring rapid acquisition and patient comfort, however, with some variance in resolution of individual lesions compared with contact-based dermoscopy standards. Conclusions: The focus stacking-based approach for noncontact dermoscopy improves the quality of diagnostic images by ensuring an all-in-focus view of differently shaped skin lesions, essential for early melanoma detection. Although the approach marks a substantial improvement in noninvasive skin imaging, the use of super-resolution techniques requires careful consideration to avoid compromising the authenticity of the raw data. This work enables the usage of advanced imaging and AI techniques in total body scanners for early melanoma detection in clinical practice.
AB - Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability. Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach. The obtained hyperfocus images should significantly enhance the diagnostic performance of total body scanners in clinical settings. Approach: We employed focus stacking to merge multiple images, each with a limited depth of field, into a single hyperfocus image, ensuring every part of the skin is in clear focus. The method was implemented in the high-resolution imaging module using an electrically tunable liquid lens to quickly capture a series of differently focused images in vivo. Algorithms were developed for image alignment, focus measurement, and fusion, with the addition of deep learning-based super-resolution techniques to further enhance image quality. A classification model was trained to provide an artificial intelligence (AI)-based lesion classification. Results: The developed optical imaging system successfully produced noncontact dermoscopic images with complete focus across all skin topographies. The hyperfocus images obtained demonstrated high resolution of formula presented and captured focus stacks at 50 frames per second, ensuring rapid acquisition and patient comfort, however, with some variance in resolution of individual lesions compared with contact-based dermoscopy standards. Conclusions: The focus stacking-based approach for noncontact dermoscopy improves the quality of diagnostic images by ensuring an all-in-focus view of differently shaped skin lesions, essential for early melanoma detection. Although the approach marks a substantial improvement in noninvasive skin imaging, the use of super-resolution techniques requires careful consideration to avoid compromising the authenticity of the raw data. This work enables the usage of advanced imaging and AI techniques in total body scanners for early melanoma detection in clinical practice.
KW - artificial intelligence
KW - focus stacking
KW - melanoma diagnostics
KW - noncontact dermoscopy
KW - skin lesion imaging
KW - total body dermoscopy
UR - http://www.scopus.com/inward/record.url?scp=105015782956&partnerID=8YFLogxK
U2 - 10.1117/1.JBO.30.9.096001
DO - 10.1117/1.JBO.30.9.096001
M3 - Article
C2 - 40927515
AN - SCOPUS:105015782956
VL - 30
SP - 96001
JO - Journal of biomedical optics
JF - Journal of biomedical optics
SN - 1083-3668
IS - 9
M1 - 096001
ER -