title: Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study.
abstract: BACKGROUND
  Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images.
  
  
  OBJECTIVE
  Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy.
  
  
  METHODS
  At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy. Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists.
  
  
  RESULTS
  On resubstitution, 100% sensitivity was achieved at 85% specificity with a 13-parameter linear classifier and 100%/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis.
  
  
  CONCLUSIONS
  Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.
authors.affiliation: Electro-Optical Sciences, Inc, Irvington, NY, USA. elbaum@eo-sciences.com
authors.forename: M
authors.initials: M
authors.lastname: Elbaum
authors.forename: A W
authors.initials: AW
authors.lastname: Kopf
authors.forename: H S
authors.initials: HS
authors.lastname: Rabinovitz
authors.forename: R G
authors.initials: RG
authors.lastname: Langley
authors.forename: H
authors.initials: H
authors.lastname: Kamino
authors.forename: M C
authors.initials: MC
authors.lastname: Mihm
authors.forename: A J
authors.initials: AJ
authors.lastname: Sober
authors.forename: G L
authors.initials: GL
authors.lastname: Peck
authors.forename: A
authors.initials: A
authors.lastname: Bogdan
authors.forename: D
authors.initials: D
authors.lastname: Gutkowicz-Krusin
authors.forename: M
authors.initials: M
authors.lastname: Greenebaum
authors.forename: S
authors.initials: S
authors.lastname: Keem
authors.forename: M
authors.initials: M
authors.lastname: Oliviero
authors.forename: S
authors.initials: S
authors.lastname: Wang
mesh_terms: D003937:Diagnosis, Differential; D005103:Expert Systems; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis*; D009508:Nevus, Pigmented / Q000175:diagnosis*; D010781:Photography; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis*; D013053:Spectrophotometry