title: Automated melanoma recognition.
abstract: A system for the computerized analysis of images obtained from ELM has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.
authors.forename: H
authors.initials: H
authors.lastname: Ganster
authors.forename: A
authors.initials: A
authors.lastname: Pinz
authors.forename: R
authors.initials: R
authors.lastname: Röhrer
authors.forename: E
authors.initials: E
authors.lastname: Wildling
authors.forename: M
authors.initials: M
authors.lastname: Binder
authors.forename: H
authors.initials: H
authors.lastname: Kittler
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis*; D008853:Microscopy / Q000379:methods; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis*