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Paul Fieguth, Trefford Simpson; Automated Measurement of Bulbar Redness. Invest. Ophthalmol. Vis. Sci. 2002;43(2):340-347.
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© ARVO (1962-2015); The Authors (2016-present)
purpose. 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
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