Gray Level Co-Occurrence Matrix (GLCM) Parameters Analysis for Pyoderma Image Variants

Abstract

Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLCM) based texture analysis of skin diseases for parametric variations. The investigations were carried out using three Pyoderma variants (Boil, Carbuncle, and Impetigo Contagiosa) using GLCM. GLCM parameters (Energy, Correlation, Contrast, and Homogeneity) were extracted for each colour component of the images taken for the investigation. Contrast, correlation, energy, and homogeneity represent the coarseness, linear dependency, textural uniformity, and pixel distribution of the texture, respectively. The analysis of the GLCM parameters and their histograms showed that the said textural features are disease dependent. The approach may be used for the identification of dermatological diseases with satisfactory accuracy by employing a suitable machine learning algorithm.