title: Modeling spatial relation in skin lesion images by the graph walk kernel.
abstract: Early skin cancer detection with the help of dermoscopic images is becoming more and more important. Previous methods generally ignored the spatial relation of the pixels or regions inside the lesion. We propose to employ a graph representation of the skin lesion to model the spatial relation. We then use the graph walk kernel, a similarity measure between two graphs, to build a classifier based on support vector machines for melanoma detection. In experiments, we compare the sensitivities and specificities of models with and without spatial information. Experimental results show that the model with spatial information performs the best in both sensitivity and specificity. Statistical test indicates that the improvement is significant.
authors.affiliation: Engineering Technology Department, University of Houston, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Situ
authors.forename: Tarun
authors.initials: T
authors.lastname: Wadhawan
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Yuan
authors.forename: George
authors.initials: G
authors.lastname: Zouridakis
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D012878:Skin Neoplasms / Q000473:pathology*