Segmentation and classification of melanocytic skin lesions using local and contextual features

This work presents a novel approach for detecting and classifying melanocytic skin lesions on macroscopic images. We oversegment the skin lesions using superpixels, and classify independently each superpixel as a benign or malignant using local and contextual information. The overall superpixel classification results allow to calculate an index of malignancy or benignity for the skin lesion. Using the proposed approach it is possible to discriminate a malignant from a benign skin lesion by recognizing early signs of malignancy in parts of the segmented skin lesion. The experimental results are promising, and show a potential accuracy of 99.34% on a popular data set, outperforming the current state-of-art methods.