The current work describes the use of multidimensional Euclidean geometric distance (EGD) and Bayesian methods to characterize and classify the sky and cloud patterns present in image pixels. From specific images and using visualization tools, it was noticed that sky and cloud patterns occupy a typical locus on the red–green–blue (RGB) color space. These two patterns were linearly distributed parallel to the RGB cube’s main diagonal at distinct distances. A characterization of the cloud and sky patterns EGD was done by supervision to eliminate errors due to outlier patterns in the analysis. The exploratory data analysis of EGD for sky and cloud patterns showed a Gaussian distribution, allowing generalizations based on the central limit theorem. An intensity scale of brightness is proposed from the Euclidean geometric projection (EGP) on the RGB cube’s main diagonal. An EGD-based classification method was adapted to be properly compared with existing ones found in related literature, because they restrict the examined color-space domain. Elimination of this limitation was considered a sufficient criterion for a classification system that has resource restrictions. The EGD-adapted results showed a correlation of 97.9% for clouds and 98.4% for sky when compared to established classification methods. It was also observed that EGD was able to classify cloud and sky patterns invariant to their brightness attributes and with reduced variability because of the sun zenith angle changes. In addition, it was observed that Mie scattering could be noticed and eliminated (together with the reflector’s dust) as an outlier during the analysis. Although Mie scattering could be classified with additional analysis, this is left as a suggestion for future work.

Introduction:We describe a teledermatology infrastructure, implemented as part of a statewide large-scale telemedicine network, designed to provide comprehensive support for examination and clinical management protocols, which includes telediagnostic and patient triage and referral. It was implemented as a web-based system and an associated mobile application that supports both the primary healthcare facility team and the specialist during the patient care process. Methods:We describe the process models, protocols and technology employed, and the requirements generated for their development. We also present the results and experiences gained in implementing the model. The teledermatology service was implemented in 313 primary healthcare facilities in 286 municipalities in the State of Santa Catarina, Brazil. This study covers the period from January 2014 to June, 2018. Results:During this period, the teledermatology infrastructure processed 83,100 teledermatology examinations, of which 75,832 were validated and employed for patient triage and clinical management. Teledermatology allowed 33,112 patients to avoid further referral and be treated locally. Of this cohort, 7,513 patients presented more complex dermatoses that could be treated at the primary care level using telehealth-supported clinical management. Conclusions:Results indicate that this model contributes not only to the resolution of cases at the primary care level but also to the progressive improvement of the technical quality of dermatological examinations performed by technical staff at the primary healthcare.