Segmentation of Scaling in Psoriasis

Psoriasis is an immune-mediated disease that affects the skin. Scaling typically appears as white or creamy colored scales on regions of red and inflamed skin (erythema) but can also appear in isolation without the accompanying erythema. Psoriasis Area and Severity Index is the most widely used tool for the measurement of severity of psoriasis .These scores are estimated by inspecting the psoriatic lesions visually and relying on the clinicians’ expertise to derive meaningful scores. It is possible for two clinicians to derive two different severity scores using the same scoring technique for the same psoriatic lesion .In this project, as an integral part of developing a reliable evaluation method for psoriasis, an algorithm is presented for segmenting scaling in 2-D digital images. This algorithm is composed of two main stages: a feature extraction stage and a scaling segmentation stage. The algorithm analyses skin color using an appropriately chosen color space and uses a bank of Gabor filters to describe the textures that correlate strongly with scaling. A scaling contrast map is developed to enhance the contrast of scaling from erythema. The algorithm next removes erythema pixels from consideration and resamples the image to collect training samples for the classification process. The scaling contrast map is applied to the image and the resulting image is processed to thresh-hold out the erythema. A training set for the scaling classifier is extracted from the image where the training set is composed of pixels that are highly likely to be scaling and pixels that are highly likely to be normal skin. The pixels are classified using a support vector machine defined by the training set and the resulting image smoothed using a Markov random field.