Summary
Objectives:
The ability of various classifiers to discriminate between normal and glaucomatous
eyes based on features derived from automated analysis of laser scanning images of
the eye background is investigated.
Methods:
To compare the classifiers without over-optimization for a given dataset, we use
a simulation model to create topography images. We designed three different simulation
setups as model of extreme situations and medical subgroups.
Results:
Neither linear nor tree-based classifiers are ideal for all setups. The most robust
performance is obtained by a combination of both, so-called Double-Bagging. Classification
of real data from a case-control study shows best results with Double-Bagging. All
results obtained with the analysis method extracting features automatically are worse
than those obtained by the same classifiers but with features derived from an analysis
method that requires intervention of a physician.
Conclusions:
Robust classification results for classification of laser scanning images obtained
with the Heidelberg Retina Tomograph are achieved by combined classifiers. The examined
automated procedure causes an increased misclassification error compared to the established
clinical routine requiring an expert physician’s intervention.
Keywords
Classification - laser scanning images - glaucoma - simulation
