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Skin texture analysis for medical diagnosis

This paper proposes an automated system for recognizing disease conditions of human skin in context to health informatics. The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM). Directional GLCMs are computed along four directions viz. horizontal, vertical, right diagonal, left diagonal, and a set of features viz. Contrast, Homogeneity, Mean, Variance and Energy computed from each, are averaged to provide an estimation of the texture class. The system is tested on a set of medical images displaying three dermatological skin conditions viz. Acne, Eczema, and Urticaria. The features are considered in various combinations viz. individually, and in joint 2-D feature spaces, using L1 and L2 metrics as well as neural network classifiers, to study which combination produces best recognition accuracies.