title: Features identification for automatic burn classification.
abstract: OBJECTIVE
  In this paper an automatic system to diagnose burn depths based on colour digital photographs is presented.
  
  
  BACKGROUND
  There is a low success rate in the determination of burn depth for inexperienced surgeons (around 50%), which rises to the range from 64 to 76% for experienced surgeons. In order to establish the first treatment, which is crucial for the patient evolution, the determination of the burn depth is one of the main steps. As the cost of maintaining a Burn Unit is very high, it would be desirable to have an automatic system to give a first assessment in local medical centres or at the emergency, where there is a lack of specialists.
  
  
  METHODS
  To this aim a psychophysical experiment to determine the physical characteristics that physicians employ to diagnose a burn depth is described. A Multidimensional Scaling Analysis (MDS) is then applied to the data obtained from the experiment in order to identify these physical features. Subsequently, these characteristics are translated into mathematical features. Finally, via a classifier (Support Vector Machine) and a feature selection method, the discriminant power of these mathematical features to distinguish among burn depths is analysed, and the subset of features that better estimates the burn depth is selected.
  
  
  RESULTS
  A success rate of 79.73% was obtained when burns were classified as those which needed grafts and those which did not.
  
  
  CONCLUSIONS
  Results validate the ability of the features extracted from the psychophysical experiment to classify burns into their depths.
authors.affiliation: Servicio de Cirugía Plástica y Grandes Quemados, Hospitales U. Virgen del Rocío, Seville, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Serrano
authors.affiliation: Servicio de Cirugía Plástica y Grandes Quemados, Hospitales U. Virgen del Rocío, Seville, Spain.
authors.forename: Rafael
authors.initials: R
authors.lastname: Boloix-Tortosa
authors.affiliation: Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Spain.
authors.forename: Tomás
authors.initials: T
authors.lastname: Gómez-Cía
authors.affiliation: Servicio de Cirugía Plástica y Grandes Quemados, Hospitales U. Virgen del Rocío, Seville, Spain. Electronic address: cserrano@us.es.
authors.forename: Begoña
authors.initials: B
authors.lastname: Acha
mesh_terms: D000465:Algorithms; D001331:Automation; D002053:Burn Units; D002056:Burns / Q000145:classification / Q000473:pathology*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010781:Photography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology*; D060388:Support Vector Machine; D015599:Trauma Severity Indices
keywords: Automatic burn depth estimation; Computer aided diagnosis (CAD); Digital photograph; Multidimensional Scaling Analysis