title: Enhanced 3D curvature pattern and melanoma diagnosis.
abstract: This article describes an enhanced curvature pattern based melanoma diagnosis system using convolution techniques and ensemble classifiers. We extract the 3D data of melanoma with a photometric stereo device first. Then differential forms of the melanoma surface can be extracted with the convolution method proposed. After extracting 3D based differential forms, statistical moments of enhanced principal curvatures of skin surfaces are calculated to describe the geometrical texture patterns. Finally, ensemble classifiers are constructed whose optimal mean sensitivity and specificity can reach 89.24 percent and 87.62 percent respectively. Comparisons with skin tilt/slant pattern based 3D shape characterization method and 2D methods like color variation and border irregularity are also included.
authors.affiliation: Machine Vision Laboratory, University of the West of England, Bristol BS16 1QY, UK; Faculty of Engineering, University of Leeds, Leeds LS2 9JT, UK. kylepub@gmail.com
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhou
authors.forename: Melvyn
authors.initials: M
authors.lastname: Smith
authors.forename: Lyndon
authors.initials: L
authors.lastname: Smith
authors.forename: Abdul
authors.initials: A
authors.lastname: Farooq
authors.forename: Robert
authors.initials: R
authors.lastname: Warr
mesh_terms: D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*