## 4 Discussion  
The proposed encoding of optical properties to components of the HSV color space was applied to two use cases: the combination of parameters from different imaging modalities (healthy and pathological aorta samples) and the combination of parameters from a single system (dystrrophic and control mice muscles). These measurements, taken with OCT, HSI and PS-OCT, allowed us to obtain relevant tissular optical properties for the samples, namely \(\mu_{t}\), \(c_{e}\) and \(\Delta n\), respectively. All distributions were centered on different values; therefore, one could theoretically use any of them for H.  
In the case of multiple-modality imaging, the OCT system provides measurements with a resolution of approximately 13 \(\upmu\)m/px, while our HSI device yields 0.9 mm/px at the working distance for the aortas. This means that \(\mu_{t}\) (Figure 1d) is the best for encoding V in the HSV color space since it has a much higher resolution and spatial variation than the HSI chromophores (Figure 1b). As discussed in Section 1, luminance (or, in this case, V) is the best dimension for the representation of high-resolution data due to the increased ability to detect patterns in luminance variations. To represent S, it is necessary to use a magnitude that is independent of the other two so that the HSV space is well defined. We used the profilometry \(p\) of the sample for this task (Figure 1c), calculated as the depth of maximum intensity in the OCT images. This means that higher-resolution and better-focused regions will generate vibrant and saturated colors, while lower-resolution and out-of-focus positions will provide less saturated and, therefore, less distracting colors. Since the aortic tissue was flat, the S values were mostly constant across both samples. For the aortas, to create the mapping of \(\mu_{t}\) to V, we used a transformation that took the maximum and minimum of all \(\mu_{t}\) values across the aorta dataset and re-scaled them to the 0-1 range of V. Then, this transformation was applied to each \(\mu_{t}\) value individually. By doing so, we kept the color scale consistent across all ROIs and samples (Figure 2). The same was done for H and S, but the re-scaled, equalized range of H was 0.3-1, to avoid the cyclic nature of H, as mentioned in Section 2. Figure 1b-d use the same perceptually uniform colormap (magma) [36], which essentially means that the selected colors have a lightness that grows linearly from 0 to 1. It is instantly clear how high-resolution data (Figure 1c,d) benefit from this type of representation, with hotspots being clearly highlighted. Discerning the elastin content variations (Figure 1b) is more difficult, due to having to distinguish between multiple shades of what is essentially the same color. On the other hand, encoding \(c_{e}\) as H makes it easier to pinpoint subtle variations, without losing the spatial information given by \(\mu_{t}\) (Figure 1e). Using the combinative visualization method allows for immediate discrimination between the two types of samples, both through the color difference between samples and also through the color uniformity (Figure 2). The co-registration with the \(\mu_{t}\) maps helps to pinpoint the structural differences in areas with different elastin content, especially when  
Figure 3: Application of the colorimetric combination to dystrophic mice muscles for a control mouse (**a**) and at different ages in the progression of the disease (**b**,**c**). In this case, we kept S and V as the profilometry and \(\mu_{t}\) of the samples but, for the H, we used their birefringence as a clinical marker. The 2D color legend is shown in (**d**), where S is not shown due to the low impact of the profilometry on visualization, since the muscles were cut flat. zooming in on one of the ROIs (Figure 1e). For example, some red/magenta areas (high \(c_{e}\)) coincide with darker colors (low \(\mu_{t}\)), but, when looking at all the images, it is clear that there is no apparent visual correlation between the attenuation and elastin content.