Macroscopic Pigmented Skin Lesion Prescreening

Abstract

Melanoma is one of the deadliest types of pigmented skin lesions, and if identified in the earlier stages can increase the patient survival rate. The use of digital cameras as an alternative to other devices, such as dermatoscope, is gaining space in skin lesion prescreening with e-health systems used in the macroscopic diagnose of pigmented skin lesion images. The traditional framework used to classify macroscopic pigmented skin lesion (MPSL) images consists of a preprocessing step to remove hair and shading effects, followed by the lesion area detection and segmentation. Next, techniques are used to extract a set of features from the obtained region, and these attributes make it possible to distinguish between malignant and benign cases. Usually, the features are extracted from data labeled by a specialist, and are used to train a machine learning algorithm, which is then used to suggest a diagnosis for an undiagnosed skin lesion image. In this work, we present a review of some of the most recent advances in MPSL segmentation and classification.