Summary
Objectives:
One method for assessing pathological retinal nerve fiber layer (NFL) appearance
is by comparing the NFL to normative values, derived from healthy subjects. These
normative values will be more specific when normal physiological differences are taken
into account. One common variation is a split bundle. This paper describes a method
to automatically detect these split bundles.
Methods:
The thickness profile along the NFL bundle is described by a non-split and a split
bundle model. Based on these two fits, statistics are derived and used as features
for two non-parametric classifiers (Parzen density based and k nearest neighbor).
Features were selected by forward feature selection. Three hundred and nine superior
and 324 inferior bundles were used to train and test this method.
Results:
The prevalence of split superior bundles was 68% and the split inferior bundles’
prevalence was 13%. The resulting estimated error of the Parzen density-based classifier
was 12.5% for the superior bundle and 10.2% for the inferior bundle. The k nearest
neighbor classifier errors were 11.7% and 9.2%.
Conclusions:
The classification error of automated detection of split inferior bundles is not
much smaller than its prevalence, thereby limiting the usefulness of separate cut-offvalues
for split and non-split inferior bundles. For superior bundles, however, the classification
error was low compared to the prevalence. Application of specific cut-offvalues, selected
by the proposed classification system, may therefore increase the specificity and
sensitivity of pathological NFL detection.
Keywords
Pattern recognition system - computer-assisted diagnosis - glaucoma - retina - morphology
