title: Diagnostic image analysis of malignant melanoma in in vivo confocal laser-scanning microscopy: a preliminary study.
abstract: OBJECTIVE
  In this study we assessed the applicability of image analysis and a machine learning algorithm on diagnostic discrimination of benign and malignant melanocytic skin tumours in in vivo confocal laser-scanning microscopy (CLSM).
  
  
  METHODS
  A total of 857 CLSM tumour images including 408 benign nevi and 449 melanoma images was evaluated. Image analysis was based on features of the wavelet transform. For classification purposes we used a classification tree software (CART). Moreover, automated image analysis results were compared with the prediction success of an independent human observer.
  
  
  RESULTS
  CART analysis of the whole set of CLSM tumour images correctly classified 97.55% and 96.32% of melanoma and nevi images. In contrast, sensitivity and specificity of 85.52% and 80.15% could be reached by the human observer. When the image set was randomly divided into a learning (67% of the images) and a test set (33% of the images), overall 97.31% and 81.03% of the tumour images in the learning and test set could be classified correctly by the CART procedure.
  
  
  CONCLUSIONS
  Provided automated decisions can be used as a second opinion. This can be valuable in assisting diagnostic decisions in this new and exciting imaging technique.
authors.affiliation: Department of Internal Medicine, Division of Oncology, Medical University Graz, Graz, Austria. armin.gerger@klinikum-graz.at
authors.forename: Armin
authors.initials: A
authors.lastname: Gerger
authors.forename: Marco
authors.initials: M
authors.lastname: Wiltgen
authors.forename: Uwe
authors.initials: U
authors.lastname: Langsenlehner
authors.forename: Erika
authors.initials: E
authors.lastname: Richtig
authors.forename: Michael
authors.initials: M
authors.lastname: Horn
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Weger
authors.forename: Verena
authors.initials: V
authors.lastname: Ahlgrimm-Siess
authors.forename: Rainer
authors.initials: R
authors.lastname: Hofmann-Wellenhof
authors.forename: Hellmut
authors.initials: H
authors.lastname: Samonigg
authors.forename: Josef
authors.initials: J
authors.lastname: Smolle
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D018613:Microscopy, Confocal / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*