| Title: | Optimizing digital image quality for improved skin cancer detection |
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| Authors: | ID Dugonik, Bogdan (Author)
ID Golob, Marjan (Author)
ID Marhl, Marko (Author)
ID Vučinič Dugonik, Aleksandra (Author) |
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| Files: |  jimaging-11-00107.pdf (5,63 MB)
MD5: 6791B3139AE8684BB9B7864061DF5ED0
 https://www.mdpi.com/2313-433X/11/4/107
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
MF - Faculty of Medicine
PEF - Faculty of Education
FNM - Faculty of Natural Sciences and Mathematics
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| Abstract: | The rising incidence of skin cancer, particularly melanoma, underscores the need for improved diagnostic tools in dermatology. Accurate imaging plays a crucial role in early detection, yet challenges related to color accuracy, image distortion, and resolution persist, leading to diagnostic errors. This study addresses these issues by evaluating color reproduction accuracy across various imaging devices and lighting conditions. Using a ColorChecker test chart, color deviations were measured through Euclidean distances (∆E*, ∆C*), and nonlinear color differences (∆E00, ∆C00), while the color rendering index (CRI) and television lighting consistency index (TLCI) were used to evaluate the influence of light sources on image accuracy. Significant color discrepancies were identified among mobile phones, DSLRs, and mirrorless cameras, with inadequate dermatoscope lighting systems contributing to further inaccuracies. We demonstrate practical applications, including manual camera adjustments, grayscale reference cards, post-processing techniques, and optimized lighting conditions, to improve color accuracy. This study provides applicable solutions for enhancing color accuracy in dermatological imaging, emphasizing the need for standardized calibration techniques and imaging protocols to improve diagnostic reliability, support AI-assisted skin cancer detection, and contribute to high-quality image databases for clinical and automated analysis. |
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| Keywords: | dermoscopy, melanoma, color analysis, color error, spectral power distribution, grey card, digital imaging standards |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 13.02.2025 |
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| Article acceptance date: | 27.03.2025 |
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| Publication date: | 31.03.2025 |
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| Publisher: | MDPI |
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| Year of publishing: | 2025 |
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| Number of pages: | 22 str. |
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| Numbering: | Vol. 11, iss. 4 [article no.] 107 |
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| PID: | 20.500.12556/DKUM-92430  |
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| UDC: | 616.5 |
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| ISSN on article: | 2313-433X |
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| COBISS.SI-ID: | 231166467 |
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| DOI: | 10.3390/jimaging11040107 |
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| Copyright: | © 2025 by the authors |
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| Publication date in DKUM: | 08.04.2025 |
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| Views: | 183 |
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| Downloads: | 10 |
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| Metadata: |    |
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| Categories: | Misc.
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