Implementation of fuzzy c-means clustering for Psoriasis Assessment on lesion erythema

Psoriasis is a skin disease that causes the appearance of reddish and scaly skin lesions. Lesion erythema, which refers to the inflammation (colour) of psoriasis lesion, is defined as one of Psoriasis Area and Severity Index (PASI) parameters. However, visual assessment by dermatologists is subjective and results in inter-rater variations. In this paper, an objective PASI erythema-scoring algorithm has been developed. The colour of lesion erythema was found to be dependent on the normal skin tone of the affected person. Normal skin tones are categorised into four groups (dark, brown, light brown and fair skins). A soft clustering is applied to solve the ambiguity problems at cluster boundaries. CIE L*a*b* data of lesions and their surrounding normal skin are used to calculate lesion erythema. The hue difference between lesion and normal skin corresponds to the lesion erythema. Two dedicated fuzzy c-means (FCM) algorithms are applied consecutively to classify normal skin tone and to score PASI erythema. 2,322 normal skin and 1,462 lesions samples from 204 recruited patients at Hospital Kuala Lumpur are used to build skin tone and PASI erythema score classifiers respectively. Agreement values between first and second assessments of 430 lesions for PASI erythema are determined to evaluate scoring performance. Kappa coefficients are found ≥ 0.70 for all skin tones (fair - 0.70, light brown - 0.8, brown - 0.79, and dark skin - 0.90). These agreement results show that the proposed method is reliable and objective, and thus can be used for clinical practices.