BACKGROUND: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it. METHODS: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm. RESULTS: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method). CONCLUSION: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.
BACKGROUND: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it. METHODS: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm. RESULTS: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method). CONCLUSION: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.
Authors: M Emre Celebi; Y Alp Aslandogan; William V Stoecker; Hitoshi Iyatomi; Hiroshi Oka; Xiaohe Chen Journal: Skin Res Technol Date: 2007-11 Impact factor: 2.365
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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: M Emre Celebi; Hitoshi Iyatomi; William V Stoecker; Randy H Moss; Harold S Rabinovitz; Giuseppe Argenziano; H Peter Soyer Journal: Comput Med Imaging Graph Date: 2008-09-19 Impact factor: 4.790
Authors: M Emre Celebi; Gerald Schaefer; Hitoshi Iyatomi; William V Stoecker; Joseph M Malters; James M Grichnik Journal: Skin Res Technol Date: 2009-11 Impact factor: 2.365
Authors: Ana Alekseenko; Anna Wojas-Pelc; Grzegorz J Lis; Alicja Furgał-Borzych; Grzegorz Surówka; Jan A Litwin Journal: Arch Dermatol Res Date: 2010-05-23 Impact factor: 3.017