Performance disparities across skin tones remain a critical challenge in dermatological artificial intelligence, largely driven by demographic imbalance in publicly available datasets. Reliable skin tone estimation is therefore essential for analyzing and mitigating potential bias. However, accurate computation of the Individual Typology Angle (ITA), a widely used metric for skin tone characterization, depends on correctly isolating healthy background skin in lesion images. This work proposes a segmentation-driven framework for robust skin tone estimation by systematically evaluating four background skin extraction strategies: Center Crop, Structured Patches, YOLO-based lesion exclusion, and SAM-based pixel-level segmentation. Experiments conducted on the HAM10000 and PAD-UFES-20 datasets analyze how these strategies influence ITA distributions, Fitzpatrick skin type categorization, and dataset skin tone composition. Results show that background extraction significantly affects tone estimation stability and subgroup representation. While automatic methods provide consistent estimates for lighter tones, darker tones remain challenging due to dataset imbalance and intrinsic overlap in ITA values under heterogeneous acquisition conditions. To support reproducible research, we also release derived skin tone annotations and curated background skin patches for the evaluated datasets, enabling further studies on fairness and bias in dermatological AI systems.