title: Objective measurement of erythema in psoriasis using digital color photography with color calibration.
abstract: BACKGROUND
  Traditional metrics for evaluating the severity of psoriasis are subjective, which complicates efforts to measure effective treatments in clinical trials.
  
  
  METHODS
  We collected images of psoriasis plaques and calibrated the coloration of the images according to an included color card. Features were extracted from the images and used to train a linear discriminant analysis classifier with cross-validation to automatically classify the degree of erythema. The results were tested against numerical scores obtained by a panel of dermatologists using a standard rating system.
  
  
  RESULTS
  Quantitative measures of erythema based on the digital color images showed good agreement with subjective assessment of erythema severity (κ = 0.4203). The color calibration process improved the agreement from κ = 0.2364 to κ = 0.4203.
  
  
  CONCLUSIONS
  We propose a method for the objective measurement of the psoriasis severity parameter of erythema and show that the calibration process improved the results.
authors.affiliation: Biomedical Engineering Department, The University of Texas at Austin, Austin, TX, USA.
authors.forename: A
authors.initials: A
authors.lastname: Raina
authors.affiliation: Biomedical Engineering Department, The University of Texas at Austin, Austin, TX, USA.
authors.forename: R
authors.initials: R
authors.lastname: Hennessy
authors.affiliation: Department of Dermatology, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
authors.forename: M
authors.initials: M
authors.lastname: Rains
authors.affiliation: Department of Dermatology, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
authors.forename: J
authors.initials: J
authors.lastname: Allred
authors.affiliation: Department of Dermatology, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Hirshburg
authors.affiliation: Department of Dermatology, The University of Texas at Austin, Dell Medical School, Austin, TX, USA.
authors.forename: D G
authors.initials: DG
authors.lastname: Diven
authors.affiliation: Biomedical Engineering Department, The University of Texas at Austin, Austin, TX, USA.|Imaging Physics Department, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: M K
authors.initials: MK
authors.lastname: Markey
mesh_terms: D000328:Adult; D002138:Calibration / Q000592:standards; D003116:Color; D003124:Colorimetry / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D004890:Erythema / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000209:etiology; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D010781:Photography / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D011565:Psoriasis / Q000150:complications / Q000175:diagnosis* / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000000981:diagnostic imaging; D055815:Young Adult
keywords: color calibration; image processing; machine learning; psoriasis