Skin Tone Optics and Quantification Methods

Light hitting skin does three things, in roughly this order: reflects off the surface, absorbs as it travels, and scatters along the way. ### Reflection (~4–7% of incident light) The sebum layer of the epidermis has a refractive index of about 1.5; air is about 1.0. The Fresnel equations give roughly 4–7% specular reflection at this interface, which is small but makes skin look glossy and what creates the highlight problems in any handheld imaging system. ### Absorption Absorption is governed by the absorption coefficient $$\mu_a(\lambda)$$, and at visible wavelengths it's dominated by two chromophores: **Melanin** (epidermis). Its absorption falls roughly exponentially with wavelength — high in the UV/blue, much lower in the NIR. Eumelanin (brown/black) and pheomelanin (red/yellow) are produced in different ratios across pigmentations. Critically, **melanocyte concentration doesn't change much with skin tone** — what changes is melanosome size, number, and the resulting volume fraction of melanin in the epidermis. Lower-pigmented skin sits around 1–2% melanosome volume fraction; darker skin can reach 40%+. That's a 20× swing in epidermal absorption from the same anatomy. **Hemoglobin** (dermis). Both oxy- and deoxy-Hb have characteristic absorption peaks: a strong **Soret band around 400–420 nm** and **green–yellow bands around 540–600 nm**. At 660 nm, deoxy-Hb absorbs more than oxy-Hb (this is the foundation of pulse oximetry). At 940 nm, the relationship inverts. Putting these together explains the pulse-ox bias quantitatively: at 660 nm, the red light has to traverse epidermal melanin first, and this loss is much larger for higher-pigmented skin than for lower. At 940 nm, melanin barely matters. So the ratio that the device interprets as oxygen saturation is partly driven by epidermal melanin — a signal it has no way to separate. ### Scattering The remaining photons scatter on their way through tissue: - **Mie scattering** dominates in the dermis, driven by filamentous proteins like collagen (and keratin in the epidermis) when scatterer size is comparable to wavelength. - **Rayleigh scattering** dominates when scatterers are much smaller than wavelength. The balance of Mie and Rayleigh, combined with absorption, determines the **effective optical path** that a photon takes — and most of that path is in the dermis. ## Measurement technique 1: Diffuse Reflectance Spectroscopy (DRS) The cleanest way to characterize skin optics in the lab is diffuse reflectance spectroscopy. The setup is a broadband light source (halogen, xenon, or white LED), a fiber-optic probe with one delivery and one collection fiber, and a spectrometer. You compute a calibrated, dimensionless diffuse reflectance: $$ R_d(\lambda) = \frac{I_{\text{skin}}(\lambda) - I_{\text{background}}(\lambda)}{I_{\text{standard}}(\lambda) - I_{\text{background}}(\lambda)} $$ where $$I_{\text{standard}}$$ is measured against a known reflectance standard. From $$R_d(\lambda)$$ you invert to recover $$\mu_a$$ and $$\mu_s'$$ — typically using lookup tables built from Monte Carlo simulations of the specific probe geometry, since closed-form analytical models don't handle layered turbid media well. DRS is **the reference technique** — accurate, well-characterized, but not exactly portable. Probes are handheld (the Konica Minolta CM700d is a common form factor), but you still need a spectrometer and a controlled measurement environment. ## Measurement technique 2: Colorimetry and the Individual Typology Angle (ITA) Colorimeters trade spectral resolution for simplicity. They measure tristimulus values through broad-band filters, then convert to **CIELAB color space** under a standard illuminant (typically D65) and observer (10°). CIELAB encodes color as: - $$L^*$$ — lightness, 0 (black) to 100 (white) - $$a^*$$ — green (–) to red (+) - $$b^*$$ — blue (–) to yellow (+) The widely used summary metric is the **Individual Typology Angle**: $$ \mathrm{ITA} = \frac{180}{\pi}\arctan\!\left(\frac{L^* - 50}{b^*}\right) $$ ITA is the angle subtended from the reference point $$(L^*=50, b^*=0)$$ to the measured $$(L^*, b^*)$$ in the $$L^*$$–$$b^*$$ plane. Empirically, skin tones cluster along a banana-shaped curve in this plane — lighter tones in the upper-right region, darker tones lower-left. As melanin content increases, ITA decreases. ### The catch: ITA is blood-confounded This was the most interesting paper in the talk for me. Harunani et al. (SPIE 2025) used a [three-layer skin model](https://doi.org/10.1117/12.3044143) — epidermis, dermis, background — driven by published absorption and scattering spectra and inverted via the adding-doubling method. They asked a clean causal question: hold one chromophore constant, sweep the other, and see where the resulting reflectance lands in $$L^*$$–$$b^*$$. Two findings that change how you should think about ITA: 1. **Iso-melanin curves form the empirical banana.** Holding melanin fixed and sweeping blood volume fraction (0.2%–7%) traces a smooth curve in $$L^*$$–$$b^*$$. Each melanin level produces its own non-overlapping curve. Stack the curves together and you reproduce the empirically observed banana — with low-melanin skin at the top and high-melanin skin at the bottom. 2. **Blood volume slides points along those curves.** Vascular maneuvers (occlusion, congestion) change ITA without changing melanin at all. So ITA is a useful *summary*, but it's not a clean estimate of melanin specifically. Layer thickness matters too — but asymmetrically. Epidermal thickness (60→120 µm) shifts the whole distribution noticeably, while dermis thickness (1→2 mm) has a much smaller effect. Epidermal structure dominates color variation. The practical implication: if you actually want to infer chromophore concentrations from color, **use the iso-melanin / iso-blood family of curves rather than ITA alone**. ITA is a coarse one-dimensional projection of an inherently two-dimensional problem. ## Measurement technique 3: Smartphone colorimetry Burrow et al. (Biophoton. Discovery 2025) showed you can [approximate a professional colorimeter with an iPhone 11](https://doi.org/10.1117/1.BIOS.2.3.032504), if you control the conditions carefully. Their pipeline (the "SITA" algorithm): 1. Capture an RGB image of the finger at fixed distance (~7 cm) and angle (perpendicular). 2. Pick a square ROI within the image (typically 3024×3024, 8-bit JPEG). 3. Normalize RGB to [0, 1] and gamma-linearize per the sRGB transfer function. 4. Convert linear RGB to **CIE XYZ** (1931 standard observer). 5. Normalize by the D65 reference white and convert to **CIELAB** with the standard nonlinearity. 6. Compute ITA per pixel and average across the ROI. Compared against a benchtop DSM-4 colorimeter, smartphone-derived ITA agreed reasonably well — but only under controlled conditions: - **Anatomic site matters.** The dorsal side of the finger spans a wider ITA range than the palmar side, making it more discriminative for skin-tone classification. - **Exposure drift.** As exposure increases, the ITA distribution shifts toward more positive values and broadens — your skin "lightens" numerically even though biology hasn't changed. - **Lighting matters.** The most stable agreement with the reference colorimeter came under **ambient lights off, flash off**, with exposure ≈ 0.7 in their setup. The takeaway: smartphone colorimetry is a feasible path to scalable, low-cost skin-tone assessment — but you have to *fix* exposure, geometry, white balance, and ambient lighting, and ideally calibrate per-device. A free-running auto-exposure smartphone capture is essentially uncalibrated. ## A note on Fitzpatrick and Monk Two qualitative skin-color scales worth knowing about: - **Fitzpatrick (I–VI)**: classifies skin by its tanning/burning response to UV. Widely used in dermatology, but only six bins, with the upper end (V, VI) covering a huge range of darker pigmentations. - **Monk Scale (1–10)**: developed by Ellis Monk, [adopted by Google](https://skintone.google/) for technology evaluation. Ten shades, with broader coverage of darker tones. Research suggests it's more inclusive and more reliable for human-rater classification of medical and consumer technologies. Neither replaces a quantitative measurement (DRS, colorimeter, ITA), but Monk in particular is a reasonable choice when you need a discrete categorical variable — for stratifying clinical trials, training datasets, or human-rater protocols. ## Take Aways: - **The pulse-ox bias has a clean physical explanation.** It isn't a black box. Once you've sat with the melanin and hemoglobin absorption spectra, the bias is almost predictable. - **One number is rarely enough.** ITA is a one-dimensional summary of a two-dimensional space; collapsing $$L^*$$ and $$b^*$$ into a single angle confounds melanin with blood volume in ways that matter clinically. - **Practical smartphone-based measurement is feasible.** The hard part is consistency — fixed geometry, fixed exposure, controlled lighting. Anything that varies the optical path or the camera response variance becomes an uncontrolled covariate. - **Calibration is upstream of fairness.** A lot of the conversation around algorithmic bias in medical imaging starts with the model. The deeper problem is often that the *measurement itself* is pigmentation-dependent before any algorithm sees the data. This is also why I find it useful in my own work to treat skin tone (and exposure, and finger curvature, and ambient light) as **first-class design variables** — not as nuisance factors to correct for downstream. --- ### Key references - Sjoding et al. *Racial Bias in Pulse Oximetry Measurement.* NEJM, 2020. [doi:10.1056/NEJMc2029240](https://doi.org/10.1056/NEJMc2029240) - Vasudevan et al. *Melanometry for objective evaluation of skin pigmentation in pulse oximetry studies.* Communications Medicine, 2024. [doi:10.1038/s43856-024-00550-7](https://doi.org/10.1038/s43856-024-00550-7) - Bajrami et al. *Human Skin Models in Biophotonics.* Adv. Healthcare Materials, 2025. [doi:10.1002/adhm.202501894](https://doi.org/10.1002/adhm.202501894) - Harunani et al. *Establishing a causal link between the physiologic range of skin chromophore concentrations and physiologically relevant regions of CIELAB color space.* SPIE 13317, 2025. [doi:10.1117/12.3044143](https://doi.org/10.1117/12.3044143) - Burrow et al. *Smartphone tristimulus colorimetry for skin-tone analysis at common pulse oximetry anatomical sites.* Biophoton. Discovery, 2025. [doi:10.1117/1.BIOS.2.3.032504](https://doi.org/10.1117/1.BIOS.2.3.032504) - Putcha et al. *Characterizing the influence of skin pigmentation on pulse oximetry.* Biophoton. Discovery, 2025. [doi:10.1117/1.BIOS.2.3.032506](https://doi.org/10.1117/1.BIOS.2.3.032506) - Del Bino & Bernerd. *Variations in skin colour and the biological consequences of UV exposure.* British Journal of Dermatology, 2013. [doi:10.1111/bjd.12529](https://doi.org/10.1111/bjd.12529) [Open Oximetry: Skin Color Quantification](https://openoximetry.org/skin-color-quantification/) is also a great living reference for this topic, with up-to-date reviews of the measurement landscape.