Photometric computer vision-aided system for psoriasis severity scoring: a preclinical study based on a mouse model of psoriasis

摘要

Psoriasis is an inflammatory cutaneous disease of unknown origin, characterized by the appearance of red, itchy, and scaly plaques of abnormal skin. One of the most important issues that guides treatment of psoriasis is to evaluate the degree of the illness determining the psoriasis and area severity index (PASI). Dermatologists usually use visual and tactile senses to assess lesion severity, involving a subjective judgment depending on the medical practitioner. For this purpose, we propose an image processing system based on photometric stereo acquisition technique and adapted analysis criteria to evaluate skin parameters. This diagnosis aided system gives an objective and accurate evaluation of skin erythema (redness), skin thickness, and scaling, which are the three clinical parameters, with the area of lesioned skin, that determine PASI score. Thus, we estimate the intensity of erythema from the albedo map and thickness from the three-dimensional estimation of the skin surface. Finally, the combination of color and geometry results allows identifying and quantifying the skin scaling. Avalidation protocol with an image database that covers all parameter variation ranges is proposed, and the results show a high correlation with dermatologist scores. (C) 2019 SPIE and IS&T