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Nicholas V. Swindale, Gordana Stjepanovic, Adeline Chin, Frederick S. Mikelberg; Automated Analysis of Normal and Glaucomatous Optic Nerve Head Topography Images. Invest. Ophthalmol. Vis. Sci. 2000;41(7):1730-1742.
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purpose. To classify images of optic nerve head (ONH) topography obtained by
scanning laser ophthalmoscopy as normal or glaucomatous without prior
manual outlining of the optic disc.
methods. The shape of the ONH was modeled by a smooth two-dimensional surface
with a shape described by 10 free parameters. Parameters were adjusted
by least-squares fitting to give the best fit of the model to the
image. These parameters, plus others derived from the image using the
model as a basis, were used to discriminate between normal and abnormal
images. The method was tested by applying it to ONH topography images,
obtained with the Heidelberg Retina Tomograph, from 100 normal
volunteers and 100 patients with glaucomatous visual field damage.
results. Many of the parameters derived from the fits differed significantly
between normal and glaucomatous ONH images. They included the degree of
surface curvature of the disc region surrounding the cup, the steepness
of the cup walls, the goodness-of-fit of the model to the image in the
cup region, and measures of cup width and cup depth. The statistics of
the parameters were analyzed and were used to construct a classifier
that gave the probability, P(G), that each image came from
the glaucoma population. Images were classified as abnormal if P(G) > 0.5. The probabilities assigned to each image
were in most cases close to 0 (normal) or 1 (abnormal). Eighty-seven
percent of the sample was confidently classified with P(G) < 0.3 or P(G) > 0.7. Within this
group, the overall classification accuracy was 92%. The overall
accuracy of the method (the mean of sensitivity and specificity, which
were similar) in the whole sample was 89%.
conclusions. ONH images can be classified objectively and dependably by an automated
procedure that does not require prior manual outlining of disc
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