Here, we propose a method for the quantitative assessment of the clinical severity of inflammatory dermatoses based on the analysis of digital RGB images of the pathological area. It involves the extraction of the texture features to monitor the dynamics of the pathology development. The proposed approach was tested in a rat model of imiquimod-induced psoriasis-like skin inflammation by comparing the extracted texture features and the resulting composite index with an overall clinical index, which reflected morphological changes in the skin. The inflammatory model was formed for six days with simultaneous recording of the experimental skin areas using a standard digital camera under fixed imaging conditions, as well as visual assessment of the severity of inflammation based on the standard PASI scoring system. In addition, histological examination was performed to confirm the formation of the psoriasis-like skin inflammation. To quantitatively characterize the images, 87 texture features were calculated, including first- and second-order ones as well as Haralick features. Based on correlation analysis, the six most informative features were selected, and a composite index was further calculated from them by the means of principal component analysis. This index corresponded to the first principal component (PC1) and demonstrated a good correlation with the cumulative PASI scores (r=-0.82). The obtained results indicate that texture analysis of standard RGB images holds great promise for objective and reproducible quantitative assessment of the severity of inflammatory dermatoses in preclinical and clinical studies. Keywords: texture analysis, Psoriasis Area and Severity Index, PASI, automated diagnostics, inflammatory dermatoses, dermatology.