title: Multi-scale classification based lesion segmentation for dermoscopic images.
abstract: This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.
authors.forename: Mani
authors.initials: M
authors.lastname: Abedini
authors.forename: Noel
authors.initials: N
authors.lastname: Codella
authors.forename: Rajib
authors.initials: R
authors.lastname: Chakravorty
authors.forename: Rahil
authors.initials: R
authors.lastname: Garnavi
authors.forename: David
authors.initials: D
authors.lastname: Gutman
authors.forename: Brian
authors.initials: B
authors.lastname: Helba
authors.forename: John R
authors.initials: JR
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D046169:Dermoscopy / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D009506:Nevus / Q000000981:diagnostic imaging / Q000473:pathology; D012867:Skin / Q000000981:diagnostic imaging* / Q000473:pathology; D012878:Skin Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology