## 5 Conclusions  
In this study, we have presented a color-space-based approach to combine optical properties in a single, information-rich visualization. The method is built upon making use of the distribution separation between two classes of clinically relevant parameters to obtain a single image that has all the advantages of its individual components at once.  
We applied it to the combination of multi-modality (OCT and HSI) and multi-parametric (PS-OCT and OCT) optical properties. When most optical properties had different distributions for the different clinical cases, the choice of each parameter as a component of the HSV color space was based on the spatial frequency of the data. By doing so, we created image maps that colored the highly structured data through the low-structured information. When the resolution of the data is the same for all parameters, the selection of the transformation to HSV is only based on the differences in data distribution between clinical categories. Since the objective is for color to be the primary label for the pathological state of the sample, we always assign to H the optical property that has distributions with the least amount of overlap between the types of samples, with the green values corresponding to those of control samples.  
Finally, the proposed combination of optical properties leads to maps with enhanced information that can be useful to clinicians to detect inter- and intra-sample differences at a single glance, but they also allow for an in-depth analysis without having to re-adapt between uses of different colormaps, thus allowing for faster decision making in environments where this might be crucial.  
Conceptualization, V.M. and O.M.C.; methodology, V.M.; software, V.M. and J.A.G.-G.; validation, V.M., J.A.G.-G. and O.M.C.; formal analysis, V.M.; investigation, V.M.; resources, O.M.C.; data curation, V.M. and J.A.G.-G.; writing--original draft preparation, V.M.; writing--review and editing, V.M., J.A.G.-G. and O.M.C.; visualization, V.M.; supervision, O.M.C.; project administration, O.M.C.; funding acquisition, O.M.C. and J.M.L.-H. All authors have read and agreed to the published version of the manuscript.  
Support for this work was provided by the projects PREVAL 21/07 (FUSIONMUSCLE) from IDIVAL; DTS22-00127 (hyPERfusionCAM) and DTS17-00055 (INTRACARDIO) funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union; and EQC 2019-006589-P and PID2019-107270 RB-C21/AEI/10.13039/501100011033 from Ministerio de Ciencia e Innovacion and FEDER, \"A way to make Europe\".  
Human aorta specimens were obtained after surgical interventions at Hospital Universitario Marques de Valdecilla (Santander, Spain) and approved by Comite Etico de Investigacion Clinica de Cantabria\" under the INTRACARDIO project (code number 2018.008). All animal procedures were performed according to the National Institute of Health Guidelines for the Care and Use of Laboratory Animals and were approved by the Hospital de la Santa Creu i Sant Pau Animal Ethics Committee.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Due to the datasets containing sensitive information that is used in their processing, the sharing of the code and/or data of this work may be possible upon reasonable email request to the authors.  
The authors would like to thank [PERSON] and [PERSON] for providing the aorta samples and also [PERSON] and [PERSON] for supplying the dystrophic muscles. The authors would also like to thank [PERSON] and [PERSON] for their involvement in these measurements during their Ph.D. theses.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HSV & Hue-Saturation-Value \\ OCT & Optical Coherence Tomography \\ PS-OCT & Polarization-Sensitive OCT \\ HSI & Hyperspectral Imaging \\ \end{tabular}