Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Dataset
This work addresses the need for fair and reliable skin color estimation in dermatology, though it is incremental as it evaluates existing methods on a new synthetic dataset.
The paper tackled the problem of measuring skin color from dermatoscopic images by evaluating four classes of image colorimetry methods on a synthetic dataset with controlled conditions, finding that segmentation-based and color quantization methods provided robust, lighting-invariant estimates, while patch-based approaches showed significant biases requiring calibration.
This paper presents a comprehensive evaluation of skin color measurement methods from dermatoscopic images using a synthetic dataset (S-SYNTH) with controlled ground-truth melanin content, lesion shapes, hair models, and 18 distinct lighting conditions. This allows for rigorous assessment of the robustness and invariance to lighting conditions. We assess four classes of image colorimetry approaches: segmentation-based, patch-based, color quantization, and neural networks. We use these methods to estimate the Individual Typology Angle (ITA) and Fitzpatrick types from dermatoscopic images. Our results show that segmentation-based and color quantization methods yield robust, lighting-invariant estimates, whereas patch-based approaches exhibit significant lighting-dependent biases that require calibration. Furthermore, neural network models, particularly when combined with heavy blurring to reduce overfitting, can provide light-invariant Fitzpatrick predictions, although their generalization to real-world images remains unverified. We conclude with practical recommendations for designing fair and reliable skin color estimation methods.