BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Owing 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 exclusive-OR measure.
BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Owing 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 exclusive-OR measure.
Authors: Guillod Joel; Philippe Schmid-Saugeon; David Guggisberg; Jean Philippe Cerottini; Ralph Braun; Joakim Krischer; Jean-Hilaire Saurat; Kunt Murat Journal: Skin Res Technol Date: 2002-11 Impact factor: 2.365
Authors: M Emre Celebi; Hassan A Kingravi; Bakhtiyar Uddin; Hitoshi Iyatomi; Y Alp Aslandogan; William V Stoecker; Randy H Moss Journal: Comput Med Imaging Graph Date: 2007-03-26 Impact factor: 4.790
Authors: M G Fleming; C Steger; J Zhang; J Gao; A B Cognetta; I Pollak; C R Dyer Journal: Comput Med Imaging Graph Date: 1998 Sep-Oct Impact factor: 4.790
Authors: M Emre Celebi; Hassan A Kingravi; Hitoshi Iyatomi; Y Alp Aslandogan; William V Stoecker; Randy H Moss; Joseph M Malters; James M Grichnik; Ashfaq A Marghoob; Harold S Rabinovitz; Scott W Menzies Journal: Skin Res Technol Date: 2008-08 Impact factor: 2.365
Authors: Hanzheng Wang; Randy H Moss; Xiaohe Chen; R Joe Stanley; William V Stoecker; M Emre Celebi; Joseph M Malters; James M Grichnik; Ashfaq A Marghoob; Harold S Rabinovitz; Scott W Menzies; Thomas M Szalapski Journal: Comput Med Imaging Graph Date: 2010-10-20 Impact factor: 4.790
Authors: S Pelin Guvenc; Robert W Leander; Serkan Kefel; Ryan K Rader; Kristen A Hinton; Sherea M Stricklin; William V Stoecker Journal: Skin Res Technol Date: 2013-06-01 Impact factor: 2.365