Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. Border detection is often the first step in the automated analysis of dermoscopy Images. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground-truth. In contrast to previous work, we utilize an objective measure, the Normalized Probabilistic Rand Index, which takes into account the variations in the ground-truth images. The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by commonly used measures.
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