  Background: 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. One of the most important steps in dermoscopy image
analysis is the automated detection of lesion borders. Although numerous
methods have been developed for the detection of lesion borders, very few
studies were comprehensive in the evaluation of their results. Methods: 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. Conclusion: The results demonstrate that
the differences between four of the evaluated border detection methods are in
fact smaller than those predicted by the commonly used XOR measure.
