title: Automated measurement of bulbar redness.
abstract: OBJECTIVE
  To examine the relationship between physical image characteristics and the clinical grading of images of conjunctival redness and to develop an accurate and efficient predictor of clinical redness from the measurements of these images.
  
  
  METHODS
  Seventy-two clinicians graded the appearance of 30 images of redness on a 100-point sliding scale with three referent images (at 25, 50, and 75 points) through a World Wide Web-based survey. Using software developed in a commercial computer program, each image was quantified in two ways: by the presence of blood vessel edges, based on the Canny edge-detection algorithm, and by a measure of overall redness, quantified by the relative magnitude of the redness component of each red-green-blue (RGB) pixel. Linear and nonlinear regressors and a Bayesian estimator were used to optimally combine the image characteristics to predict the clinical grades.
  
  
  RESULTS
  The clinical judgments of the redness images were highly variable: The average grade range for each image was approximately 55 points, more than half the extent of the entire scale. The median clinical grade was chosen as the most reliable measure of "truth." The median grade was predicted by a weighted linear combination of the edgeness and redness features of each image. The strength of the predicted association was r = 0.976, exceeding the strength of association of all but one of the 72 individual clinicians.
  
  
  CONCLUSIONS
  Clinical grading of redness images is highly variable. Despite this human variability, easily implemented image-analysis and statistical procedures were able to reliably predict median clinical grades of conjunctival redness.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada. pfieguth@uwaterloo.ca
authors.forename: Paul
authors.initials: P
authors.lastname: Fieguth
authors.forename: Trefford
authors.initials: T
authors.lastname: Simpson
mesh_terms: D003229:Conjunctival Diseases / Q000145:classification* / Q000175:diagnosis; D006801:Humans; D006940:Hyperemia / Q000145:classification* / Q000175:diagnosis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D020407:Internet; D015588:Observer Variation