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Spatial and Temporal Processing of Threshold Data for Detection of Progressive Glaucomatous Visual Field Loss
Paul G. D. Spry, PhD;
Chris A. Johnson, PhD;
Alex B. Bates;
Andrew Turpin, PhD;
Balwantray C. Chauhan, PhD
Arch Ophthalmol. 2002;120:173-180.
Objective To evaluate the effect of spatial and temporal filtering of threshold
visual field data on the ability of pointwise linear regression (PLR) to detect
progressive glaucomatous visual field loss.
Methods Longitudinal visual field data (Full-Threshold Program 30-2 test point
pattern) were simulated using a computer model of glaucomatous visual field
progression. This approach permitted construction of a "gold standard" because
matching visual field data without variability could be generated and analyzed.
Four clustered progressive defects were produced, consisting of 2, 3, 9, and
18 locations, respectively, each with progression rates of -1 and -2.5
dB/y. Pointwise linear regression was used to identify progressive test locations
(criterion for progression of statistically significant slope of  -1
dB/y, P<.05). Each visual field series was analyzed
after the following 3 procedures: (1) no filtering (unprocessed data), (2)
Gaussian spatial possessing (3 x 3 grid), and (3) temporal processing
(2 field moving average). The effect of spatial and temporal processing on
PLR discriminatory power for progression detection was quantified by comparison
with the gold standard.
Results Spatial processing reduced PLR sensitivity to levels below that achieved
for analysis of unprocessed data for small progressive defects (9 locations)
or at the low true progression rate (-1 dB/y). Under these conditions,
spatial processing caused small PLR specificity improvement. Spatial processing
only improved PLR sensitivity above unprocessed levels when progressive defects
were large and changing rapidly (progression rate of -2.5 dB/y). Temporal
processing gave consistent PLR improvement in sensitivity for all defect sizes
and true progression rates. Pointwise linear regression sensitivity gain provided
by temporal processing allowed progression to be detected 2 to 3 visual fields
earlier than for analysis of raw data. Specificity dropped slightly as a result
of temporal processing but remained at 89% or above for all conditions studied.
Conclusions Gaussian spatial processing reduces PLR discriminatory power with low
true progression rates or small progressive defect sizes and, therefore, is
of limited use for detection of progressive visual field loss. Temporal processing
improves the sensitivity of PLR and reduces the number of tests required to
detect progressive loss with minimal loss of specificity.
Clinical Relevance Image processing techniques can be applied to threshold visual field
data to enhance sensitivity or specificity of PLR for the determination of
progressive change. This investigation demonstrates that temporal processing
may assist with the detection of significant progressive visual field loss
with fewer test results than unprocessed data.
From Discoveries in Sight, Devers Eye Institute, Portland, Ore (Drs
Spry, Johnson, and Turpin and Mr Bates); the Bristol Eye Hospital, Bristol,
England (Dr Spry); and the Department of Ophthalmology, Dalhousie University,
Halifax, Nova Scotia (Dr Chauhan). The authors have no commercial, proprietary,
or financial interest in the products or companies described in this article.
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