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Canine atopic dermatitis (CAD) is a common inflammatory and pruritic skin disease associated with allergy to exogenous allergens. The regular monitoring of skin lesions is essential to execute the anti-allergic therapy successfully. Erythema is one of the most important CAD-related lesions since it represents acute skin inflammation. Previously, we studied two optical systems (i.e., multispectral and dermatoscopic devices), which could objectively estimate erythema severity. However, we did not investigate, which image sampling method for selecting erythema-representing pixels and erythema index (EI) are correlated with the visual erythema assessment the most. In this paper, we tested three image sampling methods and four EIs for erythema severity estimation in 43 dogs. We discovered that all studied sampling methods and EIs were strongly correlated (r> 0.58) with the visual CADESI-4-based erythema severity assessment. However, the highest average Spearman’s correlations coefficient r of 0.77 was achieved when the average pixel value from the two small squared image sections without the hair and pigment was considered. On the other hand, EI, which was calculated from all three RGB values, achieved the highest r of 0.78. In this study, we identified a reliable image sampling method and erythema index (EI), which are well correlated with the visual erythema estimation.
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