Melanoma Detection on Dermoscopic Images using Superpixels Segmentation and Shape-Based Features

In this work, we present a shape-based approach to automatic skin lesion segmentation and classification in dermoscopic images. We aim to differentiate three types of lesion 1) common nevi, 2) atypical nevi, and 3) melanomas by exploring the morphology features of segmented skin lesions. Our method is an attempt to design a computer-aided ABCDEs of melanoma, where the Asymmetry and Border components are estimated using morphological features. The lesions are first segmented using a super-pixel merging strategy with an RGB criterion. Later, the segmentation method was evaluated on the PH2 dataset, and compared with other state-of-the-art skin segmentation methods. The classification was also conducted on the PH2 dataset through a 10-fold cross-validation set-up with a training and testing set partition of 90% and 10% respectively. We employed logistic regression, SVM and a neural network as classification algorithms. The best performances was 86.5% on average with the neural network.