title: Development of a novel border detection method for melanocytic and non-melanocytic dermoscopy images.
abstract: Computer aided diagnosis of dermoscopy images has shown great promise in developing a quantitative, objective way of classifying skin lesions. An important step in the classification process is the lesion segmentation. Many papers have been successful at segmenting melanocytic skin lesions (MSLs) but few have focused on non-melanocytic skin lesions (NoMSLs), since the wide variety of lesions makes accurate segmentation difficult. We developed an automatic segmentation program for the border detection of skin lesions. We tested our method on a set of 107 non-melanocytic lesions and on a set of 319 melanocytic lesions. Our method achieved precision/recall scores of 84.5% and 88.5% for NoMSLs, achieving higher scores than two previously published methods. Our method also achieved precision/recall scores of 93.9% and 93.8% for MSLs which was competitive or better than the two other methods. Therefore, we conclude that our approach is an accurate segmentation method for both melanocytic and non-melanocytic lesions.
authors.affiliation: BioMaPS Institute, Rutgers, the State University of New Jersey, Piscataway, NJ, USA. kerrin@eden.rutgers.edu
authors.forename: Kerri-Ann
authors.initials: KA
authors.lastname: Norton
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Iyatomi
authors.forename: M
authors.initials: M
authors.lastname: Celebi
authors.forename: Gerald
authors.initials: G
authors.lastname: Schaefer
authors.forename: Masaru
authors.initials: M
authors.lastname: Tanaka
authors.forename: Koichi
authors.initials: K
authors.lastname: Ogawa
mesh_terms: D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008544:Melanocytes / Q000473:pathology*; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology*