feature space scaling of 2d segmented psoriasis skin images
Anuradha Balasubramaniam,Anbu Selvi
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:1 Year: 08 February,2014 Pages:29-34
Abstract
Psoriasis is a chronic inflammatory, immune mediated skin disease. Assorted techniques are used to assess psoriasis severity and to monitor therapeutic response. The PASI system of scoring employs a visual analogue scale to score the thickness, redness (erythema), and scaling of psoriasis lesions. PASI scores are subjective and suffer from indigent inter and intra-observer concurrence. A Pixel Labelling Algorithm incorporating color, contrast and image texture conjointly provide a treatment solution. This includes feature extraction and classification. The process involved is scaling segmentation of the image. The Markov random field (MRF) is used to smooth a classification from a support vector machine (SVM) that utilizes a feature space derived from image color and scaling texture. So initially the image is contrasted to find the affected area. The concentration is on the lighting and the skin type. The scaling contrast map provides an effective way to contrast image. It focuses on colour and intensity of the image. Gabor filter is used to extract the texture from the image. The final image obtained is the ground truth. The proposed system focuses on segmentation and scaling of 2D digital images of Psoriasis to detect vague scaling. An edge mapped luminance algorithm is used. Using this algorithm, the detected vague scale gives the exact amount of affected area.
Kewords
Feature Extraction Image Segmentation Markov random field MRF Support Vector Machine SVM Psoriasis Erythema
Reference
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