Principal axes-based asymmetry assessment methodology for skin lesion image analysis

Skin cancer is the most common of all cancer types and Malignant Melanoma is the most dangerous form of it, thus prevention is vital. Risk assessment of skins lesions is usually done through the ABCD rule (asymmetry, border, color and differential structures) that classifies the lesion as benign, suspicious or highly suspicious of Malignant Melanoma. A methodology to assess the asymmetry of a skin lesion image in relation to each axis of inertia, for both dermoscopic and mobile acquired images, is presented. It starts by extracting a set of 310 of asymmetry features, followed by testing several feature selection and machine learning classification methods in order to minimize the classification error. For dermoscopic images, the developed methodology achieves an accuracy of 87% regarding asymmetry classification while, for mobile acquired images the accuracy reaches 73.1%.