title: Depth data improves skin lesion segmentation.
abstract: This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.
authors.affiliation: School of Informatics, University of Edinburgh, UK. x.li-29@sms.ed.ac.uk
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Ben
authors.initials: B
authors.lastname: Aldridge
authors.forename: Lucia
authors.initials: L
authors.lastname: Ballerini
authors.forename: Robert
authors.initials: R
authors.lastname: Fisher
authors.forename: Jonathan
authors.initials: J
authors.lastname: Rees
mesh_terms: D000465:Algorithms; D003124:Colorimetry / Q000379:methods*; D003627:Data Interpretation, Statistical; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology*; D012871:Skin Diseases / Q000473:pathology*