Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2010-09-06
Skin Research and Technology 15 (2009) 444-450
Computer Science
Computer Vision and Pattern Recognition
Scientific paper
10.1111/j.1600-0846.2009.00387.x
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.
Celebi Emre M.
Grichnik James M.
Iyatomi Hitoshi
Malters Joseph M.
Schaefer Gerald
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