title: Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis.
abstract: By means of this study, a detection algorithm for the "pigment network" in dermoscopic images is presented, one of the most relevant indicators in the diagnosis of melanoma. The design of the algorithm consists of two blocks. In the first one, a machine learning process is carried out, allowing the generation of a set of rules which, when applied over the image, permit the construction of a mask with the pixels candidates to be part of the pigment network. In the second block, an analysis of the structures over this mask is carried out, searching for those corresponding to the pigment network and making the diagnosis, whether it has pigment network or not, and also generating the mask corresponding to this pattern, if any. The method was tested against a database of 220 images, obtaining 86% sensitivity and 81.67% specificity, which proves the reliability of the algorithm.
authors: Deustotech-LIFE Unit (eVIDA). University of Deusto. Avda. Universidades, 24. 48007 Bilbao, Spain. Electronic address: jlgarcia@deusto.es.
authors: Jose Luis
authors: JL
authors: García Arroyo
authors: Deustotech-LIFE Unit (eVIDA). University of Deusto. Avda. Universidades, 24. 48007 Bilbao, Spain. Electronic address: mbgarciazapi@deusto.es.
authors: Begoña
authors: B
authors: García Zapirain
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D012878:Skin Neoplasms / Q000473:pathology*; D012880:Skin Pigmentation
keywords: Machine learning; Melanoma; Pigment network; Reticular pattern; Structural analysis