## 2 Materials and Methods  
### Theoretical Model  
The attenuation coefficient is the rate of signal decay as light propagates in the tissue. It consists of two components, absorption and scattering, and evolves from local alterations of the tissue's RI which can be directly related to its physiological state and heterogeneity. In the process of absorption, energy conversion takes place while light intensity decreases. Conversely, the elastic scattering in OCT is not an energy conversion event; it involves photon backscattering and elimination of the incident wave. The above processes are characterized by the absorption and scattering coefficients, respectively, which indicate the cross section per path length. The OCT attenuation coefficient (\(\mu_{\text{OCT}}\)) reveals the loss of OCT signal with depth due to the above processes and eventually quantifies the strength of light-tissue interaction in tissues of diverse types or pathologies. The theoretical description of the OCT signal attenuation is described by models of increasing complexity [14; 15]. Still, the most widely used is the single-scattering model which is based on a single exponential decay function according to [PERSON]'s law. The model is based on the first-order Born approximation which assumes that the OCT signal is constructed by light that remains coherent after being scattered once. The model provides reliable results for weakly scattering (\(\mu_{\text{s}}<6\) mm\({}^{-1}\)), and non-absorbing media [16; 17] with an accuracy of about 0.8 mm\({}^{-1}\) for a fixed focus geometry that is typically used in clinical practice [18]. Furthermore, it allows for the spatial determination of the attenuation coefficient of layered samples [19]. The decay of the OCT signal intensity I(z), to a depth z, is given by Equation (1):  
\[\mathrm{I(z)}\propto\,\mathrm{e}^{-2\mu_{\mathrm{OCT}}z} \tag{1}\]  
where 2z is the propagation distance in the attenuating tissue which is traversed twice due to the round-trip backscattering event. By differentiating, Equation (2) is received  
\[\mu_{\mathrm{OCT}}=-\frac{\mathrm{d}\,\mathrm{ln}(\mathrm{z})}{2\,\mathrm{dz}} \tag{2}\]  
and \(\mu_{\mathrm{OCT}}\) is calculated by fitting the exponential function to the imaging OCT data over a certain depth range [20] as:  
\[\mu_{\mathrm{OCT}}=-\frac{\mathrm{I(z)}}{2\,\Delta\sum_{z+1}^{\mathrm{n}} \mathrm{I(z)}} \tag{3}\]  
where \(\Delta\) is the registered pixel size and z is the z-th pixel along the depth in the region of interest (ROI) assuming that the attenuation is complete by the nth pixel. Under this approach, the power signal is mapped to a grey-scale value. As the ROI of the OCT tissue image must be selected prior to fitting the exponential curve, we defined the ROI depth limit at the dermo-epidermal junction as this was imposed by the requirement of using at least 15 pixels of the extracted layers for curve fitting [21]. Furthermore, the impact of multiple scattering effects is effectively diminished when only the first 0.19 mm of the OCT signal are considered for fitting [22]. The single scattering OCT model does not account for speckle effects, which typically confound OCT data. Speckle noise is a grainy pattern of bright and dark spots (i.e., signal amplitude fluctuations) on the OCT image due to the multiple ray scattering within tissue coherence distance with random amplitude and phase. The suppression of speckle noise while preserving contrast, is still an active field of research. Typical post-processing software techniques that aim to reduce OCT speckle noise are based on non-local methods. Specifically, the non-local means (NLM) algorithm evaluates the similarity of the intensity and edge information in an image, within areas around the ROI, into weights to perform a weighted maximum likelihood estimation of the noise-free image [23; 24]. Here, we employed the NLM denoising algorithm using the optimized opencv python implementation (function: cv.fastNIMeansDenoising() with filterStrength = 10, templateWindowSize = 7, and searchWindowSize = 21) [25; 26].