title: Border detection in dermoscopy images using hybrid thresholding on optimized color channels.
abstract: Automated border detection is one of the most important steps in dermoscopy image analysis. Although numerous border detection methods have been developed, few studies have focused on determining the optimal color channels for border detection in dermoscopy images. This paper proposes an automatic border detection method which determines the optimal color channels and performs hybrid thresholding to detect the lesion borders. The color optimization process is tested on a set of 30 dermoscopy images with four sets of dermatologist-drawn borders used as the ground truth. The hybrid border detection method is tested on a set of 85 dermoscopy images with two sets of ground truth using various metrics including accuracy, precision, sensitivity, specificity, and border error. The proposed method, which is comprised of two stages, is designed to increase specificity in the first stage and sensitivity in the second stage. It is shown to be highly competitive with three state-of-the-art border detection methods and potentially faster, since it mainly involves scalar processing as opposed to vector processing performed in the other methods. Furthermore, it is shown that our method is as good as, and in some cases more effective than a dermatology registrar.
authors.affiliation: Department of Electrical and Electronic Engineering, NICTA Victoria Research Laboratory, Universty of Melbourne, Parkville, Melbourne, Victoria 3010, Australia. r.garnavi@ee.unimelb.edu.au
authors.forename: Rahil
authors.initials: R
authors.lastname: Garnavi
authors.forename: Mohammad
authors.initials: M
authors.lastname: Aldeen
authors.forename: M Emre
authors.initials: ME
authors.lastname: Celebi
authors.forename: George
authors.initials: G
authors.lastname: Varigos
authors.forename: Sue
authors.initials: S
authors.lastname: Finch
mesh_terms: D003124:Colorimetry / Q000379:methods*; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*