BACKGROUND: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.
BACKGROUND:Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS:Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.
Authors: F Nachbar; W Stolz; T Merkle; A B Cognetta; T Vogt; M Landthaler; P Bilek; O Braun-Falco; G Plewig Journal: J Am Acad Dermatol Date: 1994-04 Impact factor: 11.527
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; Y Alp Aslandogan; William V Stoecker; Hitoshi Iyatomi; Hiroshi Oka; Xiaohe Chen Journal: Skin Res Technol Date: 2007-11 Impact factor: 2.365
Authors: Mounika Lingala; R Joe Stanley; Ryan K Rader; Jason Hagerty; Harold S Rabinovitz; Margaret Oliviero; Iqra Choudhry; William V Stoecker Journal: Comput Med Imaging Graph Date: 2014-04-03 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