Automated detection of wedge-shaped defects in polarimetric images of the retinal nerve fibre layer.
OBJECTIVE
Automated glaucoma detection in images obtained by scanning laser polarimetry is currently insensitive to local abnormalities, impairing its performance. The purpose of this investigation was to test and validate a recently proposed algorithm for detecting wedge-shaped defects.


METHODS
In all, 31 eyes of healthy subjects and 37 eyes of glaucoma patients were imaged with a GDx. Each image was classified by two experts in one of four classes, depending on how clear any wedge could be identified. The detection algorithm itself aimed at detecting and combining the edges of the wedge. The performance of both the experts and the algorithm were evaluated.


RESULTS
The interobserver correlation, expressed as ICC(3,1), was 0.77. For the clearest cases, the algorithm yielded a sensitivity of 80% at a specificity of 93%, with an area under the ROC of 0.95. Including less obvious cases by the experts resulted in a sensitivity of 55% at a specificity of 95%, with an area under the ROC of 0.89.


CONCLUSIONS
It is possible to automatically detect many wedge-shaped defects at a fairly low rate of false-positives. Any detected wedge defect is presented in a user-friendly way, which may assist the clinician in making a diagnosis.
Glaucoma Service, Rotterdam Eye Hospital, Schiedamsevest 180, NL-3011 BH Rotterdam, The Netherlands. koen@qi.tnw.tudelft.nl
K A
KA
Vermeer
N J
NJ
Reus
F M
FM
Vos
A M
AM
Vossepoel
H G
HG
Lemij
D000465:Algorithms; D003941:Diagnostic Techniques, Ophthalmological; D005260:Female; D005901:Glaucoma / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology*; D015588:Observer Variation; D009898:Optic Disk / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000175:diagnosis* / Q000209:etiology; D012165:Retinal Ganglion Cells / Q000473:pathology*; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index