Dictionary-based Sparse Representations for Automatic Processing and Analysis of Melanocytic Skin Lesions in Macroscopic Images

Abstract

Melanoma is the most lethal type of skin cancer, since it is most prone to metastasis. Specifically, the rate of patients who survive at least five years after early stage diagnosis of this disease is over 99%. However, this rate decreases to about 25% if detection occurs only at the last stage. In this context, systems that assist in the early diagnosis of melanoma can play an extremely important role, especially in regions where access to dermatologists is poor. However, differentiating melanoma from benign melanocytic lesions can be a challenging task, even for experienced specialists. To address this problem, in this thesis, an automatic system is proposed for melanoma detection from a simple digital photograph, which is based on sparse representation models. The results presented by the proposed system are promising and suggest that it can potentially outperform state-of-the-art alternatives and even trained dermatologists.