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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Related Experiment Video

Updated: May 10, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Optimizing Digital Image Quality for Improved Skin Cancer Detection.

Bogdan Dugonik¹, Marjan Golob¹, Marko Marhl^2,3,4

¹Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, Slovenia.

Journal of Imaging

|April 25, 2025

Summary

Improving dermatological imaging accuracy is crucial for early skin cancer detection. This study found significant color inaccuracies in various cameras and lighting, offering practical solutions for better diagnostic reliability.

Keywords:

color analysis color error dermoscopy digital imaging standards grey card melanoma spectral power distribution

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Published on: August 30, 2013

Area of Science:

Dermatology
Medical Imaging
Color Science

Background:

Rising skin cancer incidence necessitates enhanced diagnostic tools.
Current dermatological imaging faces challenges in color accuracy, distortion, and resolution, impacting diagnosis.
Accurate color reproduction is vital for reliable skin lesion assessment.

Purpose of the Study:

To evaluate color reproduction accuracy of different imaging devices and lighting conditions in dermatological imaging.
To identify sources of color discrepancies in dermatological image acquisition.
To propose practical solutions for improving color accuracy in clinical settings.

Main Methods:

Utilized a ColorChecker test chart to measure color deviations (ΔE*, ΔC*, ΔE00, ΔC00).
Assessed the influence of light sources using the Color Rendering Index (CRI) and Television Lighting Consistency Index (TLCI).
Evaluated color accuracy across mobile phones, DSLRs, and mirrorless cameras under various lighting conditions.

Main Results:

Significant color discrepancies were observed among different camera types (mobile, DSLR, mirrorless).
Inadequate dermatoscope lighting systems were found to exacerbate color inaccuracies.
Non-linear color differences (ΔE00, ΔC00) provided a more sensitive measure of color deviation.

Conclusions:

Standardized calibration techniques and imaging protocols are essential for improving dermatological image quality.
Practical solutions like manual adjustments, grayscale references, post-processing, and optimized lighting can enhance color accuracy.
Improved color accuracy supports reliable AI-assisted skin cancer detection and high-quality image databases.