用合成数据评估皮肤颜色测量方法，发现分割和量化法最抗光照干扰。
Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Dataset
- 基于分割和颜色量化的方法能稳定估计肤色
- 补丁法受光照影响大需校准，神经网络需模糊处理防过拟合
- 适合开发可靠肤色评估工具的研究者参考
本文使用包含18种不同光照条件的合成数据集（S-SYNTH），对四种图像色彩计量方法（基于分割、补丁、颜色量化及神经网络）在皮肤颜色测量中的表现进行了全面评估。该数据集具有受控的真实色素含量、病灶形状和毛发模型。实验目标是估算个体分型角（ITA）和弗氏皮肤类型。结果表明，基于分割和颜色量化的方法在光照变化下表现稳健且不变；而基于补丁的方法存在显著光照依赖偏差，需校准。神经网络模型在加入大量模糊处理以减少过拟合后，可实现光照无关的弗氏类型预测，但其在真实图像上的泛化能力尚未验证。研究最后提出设计公平可靠的皮肤颜色估计方法的实用建议。
原文摘要 · Abstract (English)
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.
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