Erythema detection in digital skin images

In this work, we present a 3-layer segmentation scheme for automatic erythema detection. First, a skin region is detected with a histogram-based Bayesian classifier. Next, the extracted skin image is represented in terms of melanin and hemoglobin components based on Independent Component Analysis (ICA). At last, a trained Support Vector Machine (SVM) is applied to identify erythema areas using feature attributes from hemoglobin and melanin component images. Experiment results on our database demonstrate the effectiveness of the proposed method. This work is motivated by the need of objective assessment of psoriasis treatment for study of psoriasis therapy. Distribution of abnormal redness on skin is an important sign in evaluation of psoriasis severity, but in practice it is determined subjectively by dermatologists. Our method can be used in a therapy evaluation system to assess treatment objectively and quantitatively.