title: Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions.
abstract: OBJECTIVE
  Several systems for the diagnosis of melanoma from images of naevi obtained under controlled conditions have demonstrated comparable efficiency with dermatologists. However, their robustness to analyze daily routine images was sometimes questionable. The purpose of this work is to investigate to what extent the automatic melanoma diagnosis may be achieved from the analysis of uncontrolled images of pigmented skin lesions.
  
  
  METHODS
  Images were acquired during regular practice by two dermatologists using Reflex 24 x 36 cameras combined with Heine Delta 10 dermascopes. The images were then digitalized using a scanner. In addition, five senior dermatologists were asked to give the diagnosis and therapeutic decision (exeresis) for 227 images of naevi, together with an opinion about the existence of malignancy-predictive features. Meanwhile, a learning by sample classifier for the diagnosis of melanoma was constructed, which combines image-processing with machine-learning techniques. After an automatic segmentation, geometric and colorimetric parameters were extracted from images and selected according to their efficiency in predicting malignancy features. A diagnosis was subsequently provided based on selected parameters. An extensive comparison of dermatologists' and computer results was subsequently performed.
  
  
  CONCLUSIONS
  The KL-PLS-based classifier shows comparable performances with respect to dermatologists (sensitivity: 95% and specificity: 60%). The algorithm provides an original insight into the clinical knowledge of pigmented skin lesions.
authors.affiliation: Department of Signal Processing and Electronic Systems, Plateau de Moulon, Gif-sur-Yvette, France. arthur.tenenhaus@supelec.fr
authors.forename: Arthur
authors.initials: A
authors.lastname: Tenenhaus
authors.forename: Alex
authors.initials: A
authors.lastname: Nkengne
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Horn
authors.forename: Camille
authors.initials: C
authors.lastname: Serruys
authors.forename: Alain
authors.initials: A
authors.lastname: Giron
authors.forename: Bernard
authors.initials: B
authors.lastname: Fertil
mesh_terms: D000465:Algorithms; D003124:Colorimetry; D016208:Databases, Factual; D003657:Decision Making; D003880:Dermatology / Q000592:standards* / Q000706:statistics & numerical data; D046169:Dermoscopy / Q000379:methods* / Q000592:standards* / Q000706:statistics & numerical data; D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D008545:Melanoma / Q000473:pathology*; D009506:Nevus / Q000473:pathology*; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*; D012880:Skin Pigmentation