A computational approach for detecting pigmented skin lesions in macroscopic images

Skin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising.