(Investigative Ophthalmology and Visual Science. 2002;43:340-347.)
© 2002
by The Association for Research in Vision and Ophthalmology, Inc.
Automated Measurement of Bulbar Redness
Paul Fieguth1 and
Trefford Simpson2
1 From the Department of Systems Design Engineering and the
2 School of Optometry, University of Waterloo, Waterloo, Ontario, Canada.
 |
Abstract
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PURPOSE. To examine the relationship between physical image characteristics and
the clinical grading of images of conjunctival redness and to develop
an accurate and efficient predictor of clinical redness from the
measurements of these images.
METHODS. Seventy-two clinicians graded the appearance of 30 images of redness on
a 100-point sliding scale with three referent images (at 25, 50, and 75
points) through a World Wide Web–based survey. Using software
developed in a commercial computer program, each image was
quantified in two ways: by the presence of blood vessel edges, based on
the Canny edge-detection algorithm, and by a measure of overall
redness, quantified by the relative magnitude of the redness component
of each red-green-blue (RGB) pixel. Linear and nonlinear regressors and
a Bayesian estimator were used to optimally combine the image
characteristics to predict the clinical grades.
RESULTS. The clinical judgments of the redness images were highly variable: The
average grade range for each image was approximately 55 points, more
than half the extent of the entire scale. The median clinical grade was
chosen as the most reliable measure of "truth." The median grade
was predicted by a weighted linear combination of the edgeness and
redness features of each image. The strength of the predicted
association was r = 0.976, exceeding the strength of
association of all but one of the 72 individual clinicians.
CONCLUSIONS. Clinical grading of redness images is highly variable. Despite this
human variability, easily implemented image-analysis and statistical
procedures were able to reliably predict median clinical grades of
conjunctival redness.
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Introduction
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The clinical judgment of ocular redness is complex and
poorly understood. Typically, the appearance of the eye is judged based
on a scale, and the examination of these scales provides a lesson in
contemporary views of measurement. Even the simplest binary descriptive
scale (red and not red) may be regarded as quantitative with the data
provided being either nominal or ordinal.1
Other
classifications include those based on the underlying reference of the
scale (verbal or visual) and the numerical basis of the scale, whether
discrete2
or continuous.3
Theoretical examination aside, the scales themselves are typically
poorly described and with few exceptions have been
untested.2
3
In addition to a lack of understanding of the
scales themselves, there is no empiric information about how
clinicians make judgments of redness. Indeed, our data show evidence to
suggest that clinicians quote wildly inconsistent grades, even in the
presence of a well-defined grading scheme. Figure 1
summarizes the
motivation of this article: Arrangements4
were made for 72
clinicians to grade the clinical appearance of the redness of 30
different pictures of conjunctivas. The figure shows the results
arranged in order of ascending median redness (solid line), and plots
the quartile ranges. As is apparent, for each image the range of
redness estimated by the graders was at least 25% of the total scale,
and on average in excess of 55% of the total scale. These results
clearly show the extremely poor quantitative accuracy of such clinical
grading, and the degree of subjectivity that is present in human
grades. In light of the data in Figure 1
, this article presents an
automated, objective alternative.
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Figure 1. The extent of variations in human grading.
Bottom to top: the five curves indicate
the minimum, 25th percentile, median, 75th percentile, and maximum
grade for each of 30 images. The scale was defined using three
benchmark images, at grades of 25, 50, and 75 points, indicated by
circles.
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Clinical grading may be judged using at least two general strategies.
The first is primarily luminance–chromaticity based. Judgments are
made on the basis of the overall redness and brightness (luminance) of
the eye. As the redness increases, so the luminance decreases. A second
strategy is made on the basis of the appearance of the visible vessels.
This could include judgments of the diameter of vessels, vessel
tortuosity, and the proportion or number of vessels occupying the area
to be graded. The difference between these methods is really one of
scale: Luminance judgments would correlate with vessel-appearance if
the capillary beds giving rise to the conjunctival flush were resolved.
Similarly, with a sufficiently low resolution, smaller vessels would
not be resolved and would "blend" into the background redness. For
any typical clinical observation, however, each type of judgment is
possible and could vary (to a large extent) independently of the other.
The automated and objective approach proposed in this article is based
on the same two criteria: Two features are extracted from each image,
one based on redness and the other based on the appearance of blood
vessels.
Because of the vagaries of clinical scales and grading, there have been
a number of attempts to perform clinical grading using automated
methods. These have typically involved examining the structure in a
particular area to determine the characteristics of the
vessels.5
6
7
8
Most recently, Papas9
showed
that the clinical grade of redness of a relatively small patch of
conjunctiva was strongly linearly associated with an automated
technique that measured the number of vessels in the patch. This very
interesting result is unfortunately difficult to relate to clinical
grading, however, because the regions evaluated were relatively small
and the task was somewhat different from typical clinical grading
where, usually, almost the whole nasal and temporal bulbar area is
graded.2
3
10
This was a study of the relationship between clinical grading and
quantitative aspects of conjunctiva images, with the goal of developing
an automated estimator for conjunctival hyperemia. The purpose of the
estimator is to reproduce the overall trend but to eliminate the
inconsistent and irreproducible details of the clinical ratings.
Quantifiable features were correlated with the clinical grading data to
produce an estimator that is accurate, consistent, and repeatable.
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Methods
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Data Collection
Thirty images of bulbar redness were used. These ranged in redness from normal
to severe. The images were derived from frontal photographs taken with
constant magnification and diffuse white illumination and included
enough of the lateral and medial canthi to recognize nasal and temporal
bulbar conjunctiva. The images rated as the least and the most red are
shown in Figure 2
. A Web site was developed4
to display the images and to
collect the ratings. The observers were required to grade each image,
presented in a random order, on a 0- to 100-point scale, using sliders
for both the nasal and temporal bulbar areas. Because of the
inconsistencies between the computer monitors of different graders, for
example the brightness, contrast, and gamma settings, each page of the
survey included three smaller images that in a previous
experiment3
were shown to represent approximately the
levels 25, 50, and 75 on a 100-point scale. Figure 3
shows a typical display and rating page. The whole survey could be
completed in approximately 10 minutes, and therefore user tedium should
not have had a significant impact on the quality of the collected
ratings.
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Figure 3. A screen shot from the survey web site. The three benchmark images are
always visible to the user at the bottom of the screen.
The two grades (temporal and nasal) are set using the sliders on the
right.
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Image Analysis
The least obvious step in our analysis is the determination of
quantitative, mathematical aspects of an image of the eye that
correlate with the grades as assessed by clinicians. The survey on the
Web site permitted respondents to describe the criteria by which they
passed judgment; however, even relatively precise statements such as
"average artery width" or "average redness" are not readily
represented as an image-processing algorithm because of the vast number
of subjective and subconscious operations undertaken by the human
visual system.
Instead, we propose to analyze redness on the basis of two
straightforward features, based on a model of the trauma mechanism.
Conjunctival hyperemia is characterized by the expansion of small
arteries just below the surface of the eye. As the blood vessels swell
they become much larger and easily detected as a red line on a white
scleral background. We propose to use an edge detector (specifically,
the Canny method11
) to measure the total length of visible
arteries. However, the smallest arteries are resolvable neither by the
pixels in a charge-coupled device (CCD) camera, nor by the human eye,
and a mild onset of hyperemia therefore begins as a diffuse reddening
with no discernible edges. In such cases, we propose an integrated
measure of redness.
We do not maintain that these represent the "optimum" features.
Rather, the rationale is that if the performance using just these
simplistic methods is good, then clearly additional study and criterion
refinement can only lead to a further improvement in the results.
Each image (I), with composite components
(IR,
IG,
IB; for red, green, and blue), is
segmented into two nasal and temporal subsets
(St,
Sn, respectively) to allow the two
sides to be analyzed separately. The redness feature
 | (1) |
represents the average integrated redness in the subimage
(S). Note that black pixels, which have no defined color,
have been removed from S. The denominator term normalizes
the feature, so that -0.5 < fr
< 1
where fr is the redness
feature. Even the most seriously traumatized eye is not completely red.
The feature range for the images in our experiment was approximately
0 
fr 0.25.
The edge feature
 | (3) |
returns the fraction of pixels that are identified as
edges—that is, the ratio of the number of edge pixels, computed by a
Canny edge detector to the total number of pixels S . The
premise of the edgeness feature is that perceived redness is not just a
function of average color, but is also the number or density of
arteries.
In preparing the segmented subsets Sn,
St, care had to be taken to eliminate
skin pixels with reddish hue that would bias redness feature results,
surrounding hair, whose strong contrast would affect the edge feature,
the pupil, and the iris. To ensure the accuracy of the results, this
segmentation was carefully performed by hand, although automating this
step should be straightforward, because the color of the sclera is
quite distinct from its surroundings.
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Results
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We collected two sets of data: the grades from clinicians through
the survey and the computed values of the redness
(fr) and edge
(fe) features.
Grading Analysis
Each of the 30 different eye images was graded by 72 clinicians.
Although the results show a broad degree of consensus, there was
astonishing variability from person to person, shown in Figure 1
. The
average range in the grades was 55—more than half the entire range of
the scale. Even the three calibration images were no exception,
Although users were told to grade the middle calibration image as 50,
the grades assigned to that image had a tremendous range, from 22 to
90. This range is not due to the tedium of completing the survey,
because the variability was not observed to increase with the position
(early versus late) in the survey.
The histogram of the distribution of assigned grades around the median
is shown in Figure 4
. The distribution is vaguely Gaussian, with an odd superposition of
spikes. Further analysis of the data shows that the spikes are due to
the human preference for round numbers. Multiples of five are more than
four times as prevalent as other numbers, lending further support to
the need for an objective grade.
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Figure 4. The median-removed distribution of grades. The distribution is
approximately Gaussian, with an odd periodicity, due to the human bias
toward round numbers (multiples of 5 and 10).
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The SD of the grading distribution for each eye varies between
approximately 6% and 15%. The trimmed SD, based on keeping only the
50% most consistent grades, is considerably tighter (as implied in
Fig. 1 ), at 2.9% to 8%.
Feature Regression
Figures 5
and 6
show the raw data points of the redness and edgeness features,
respectively, versus the median human grade. There is a clear
relationship between the features and the human data, although the
relationship is not necessarily linear (especially for
fr), and may have varying degrees of
consistency (for example, the three or four outliers in the edgeness
data in Fig. 6
).
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Figure 5. Temporal image grades plotted against the integrated redness feature
(fr). Solid
line: hyperbolic regression (equation 5)
; dashed lines:
the empirical 1-SD envelope around the regression.
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Figure 6. Nasal image grades plotted against the edge fraction feature
(fe). Solid line:
linear regression (equation 6)
, computed omitting the outlying images
at large grades. Dashed lines: empirical 1-SD envelope
around the regression.
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Our goal is to predict, in some fashion, the grade from the extracted
image data. We denote by 
(f)
the estimated grade g based on feature value f.
Clearly, we want to constrain the grade to lie within the scale
 | (4) |
The solid lines in the Figures 5
and 6
represent the chosen
regressions. Because of the wide range of
fr (up to 1.0), a linear fit is
inappropriate, and a hyperbolic regression was therefore chosen for
fr, having an asymptote at
g = 100 and a slope at the origin of 45/0.05.
Although unenlightening, for completeness the temporal redness
regression follows
 | (5) |
Although this may appear overfit, the equation was fit by
adjusting only one free parameter, once the slope and asymptote were
specified.
A more straightforward linear regression was chosen for
fe, where the three misfitting data
points were eliminated from the coefficient learning process. The
resultant expression for the nasal edgeness regression is
 | (6) |
With two estimators
(fr),
(fe) defined,
there is clearly an ambiguity regarding which estimator to use or
whether the estimators can somehow be combined automatically. If
(fr),
(fe) are
viewed as approximate "measurements" of the true grade
g, then under certain conditions the optimal linear
Bayesian estimate of the grade is
 | (7) |
and the associated estimation error variance is
 | (8) |
where 
e2, r2 are the error
variances of the single-feature estimators
(fe),
(fr)
respectively. (Ideally,
(fr),
(fe) should
be unbiased estimates of grade g, and the errors in the two
estimates are assumed to be independent.) These error variances cannot
be deduced theoretically, but have to be inferred from the data. We
computed them as the smoothed local sample variance of the human grades
around the regressed curves. The resultant 1-SD curves are shown in
Figures 5
and 6 . Clearly the Bayesian estimator7
biases in
favor of estimator
(fe) for eyes
having only mild redness, and toward
(fr) for
severe redness.
These developments were discussed and illustrated, for compactness,
based on only one half of the data, ignoring the nasal redness and
temporal edgeness cases. In the following results all the data are
used.
Figure 7
shows the estimation results, using the Bayesian
estimator7
for both the temporal and nasal data. The
estimation results lie very close to the dashed-line ideal, with a
correlation coefficient of 0.976 between the estimates and the human
medians. For comparison purposes, an equivalent plot is shown in Figure 8 , where a statistical sample of the human grades is plotted against the
median, for a corresponding correlation coefficient of only 0.841.
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Figure 7. Estimator performance for both nasal and temporal data; the correlation
coefficient of the fit is 0.976. The fit is best for low grades, for
which the most data were available.
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Figure 8. Clinical grades, plotted in the same manner as in Figure 7
. Clearly,
the automated approach yields considerably improved repeatability and
error variances.
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The error bars in Figure 7
are unit SD in length, based on the Bayesian
error variance.8
If the error variances are accurate, they
should meaningfully reflect the distribution of the estimates
around the true value g—that is,
 | (9) |
should be zero-mean, unit-variance Gaussian. Experimentally, the
distribution in equation 9
was found to be approximately Gaussian, with
a mean of -0.04 and a variance of 1.01, clearly validating the
estimated error variances.
Figure 9
compares the error SDs associated with the grading estimates of
individuals, the 50% most consistent individuals, and our proposed
automated system. Our system represents a great reduction in error over
the individuals and except for cases of severe redness, where our
regression and learning have a paucity of data, our errors are
competitive with the 50% set. Finally, Figure 10
shows the performance of each individual, compared with our proposed
system. Of the 72 clinicians who took part in the experiment, only 1
was able to match the consistency (measured as the correlation
coefficient) of our proposed method. Clearly, our errors are
competitive with or better than even the most consistent graders.
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Figure 10. The performance of individual clinicians relative to the proposed
system. Of 72 respondents, all but one performed more poorly than our
feature-based automated system. Dashed line: correlation
of 0.976 attained by the automated system.
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Discussion
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The results of this study reinforce previous results. Automated
measures provide information that is linearly associated with
subjective grades of redness. Our results are similar to those of
Willingham et al.6
and Papas,9
in that we
each found strong associations between the subjective grades and the
measurements, as opposed to the weaker associations of Guillon and
Shah.5
Our methodology is more similar to that of
Willingham et al. and Papas, inasmuch as we used images that were
graded, whereas Guillon’s and Shah’s subjective data were collected
in vivo with a slit lamp biomicroscope. Our methods differ from those
of previous workers who have either not used first-order (overall
redness) information or have used it separately from second-order
(vessel attribute) information. We provide a novel, straightforward
method for the combination of image features that is remarkably
concordant with the grades assigned by clinicians.
A primary objective of this study was to minimize the required operator
intervention. In some previous studies combinations of custom software
and hardware have been used, making the analysis inaccessible or
expensive to develop, operate, and maintain. For our research, commonly
used desktop computers (Pentium processor; Intel, Mountain View, CA;
running Windows; Microsoft, Redmond, WA) performed the data
acquisition, and numeric processing was performed in an environment
based on a widely available program (Matlab; MathWorks, Natick, MA).
Operator intervention was limited to a few mouse clicks to assist in
the segmentation of the eye, removing the lids and the corneal
components of the images. These processing steps can therefore be
implemented almost universally.
The way we chose to obtain grading was somewhat unusual and perhaps
controversial, in that we were unable to control our observers and that
our sampling method was far from randomized. These experimental
attributes are no different, however, from any of the previous reports
comparing automated methods to subjective methods of grading. Our
sampling method provided additional diversity, in that the clinicians
were not from a single institution. The associated diversity in skill
set also provided a more realistic sense to the grading data, in that
not all graders were true experts who used grading scales many times
per day. In other words, despite these additional sources of
variability, the clinical data were still remarkably well predicted by
the proposed automated measures.
The introduction stated that very little is known about grading
techniques. Of particular importance in this regard is Figure 4
,
showing clear peaks at decimal and mid-decimal values, similar to the
effects observed in the literature.12
This was not
accomplished by accident, because the graders would have had to
carefully adjust a slider to generate these numbers. This suggests
strongly that there is a tendency not to use the many steps on a
100-point scale and that, perhaps, all that is required is a 20-point
redness scale. There are theoretical and practical implications of
this,13
14
but the exact impact on the accuracy and
repeatability of redness grading would have to be determined
empirically.
Another result relating to grading was the large range of grading
associated with the reference images that were part of the data set
that clinicians graded. Although the median grades were very similar to
the reference grades (Fig. 1)
, there was a surprisingly large range of
grades associated with each reference. This suggests that the
clinicians either could not psychophysically match grades to the
references, an unlikely conclusion, or that they chose to ignore the
values assigned to the reference. Clinicians have been shown to resist
using tools that are assistive,15
and this is perhaps a
manifestation of this phenomenon. The clinician disagrees with the
grade assigned to the reference and simply ignores it.
The results show that there were strong associations between the
computed and clinically assigned grades. Figures 5
and 6
illustrate the
distribution and variability of the individual computed grades. The
error bars on Figure 7
show the variability of the combined computed
grade for each image. In comparison, estimates of the variability of
the clinical graders’ performance are illustrated in Figure 8
, using
resampling techniques.16
The point illustrated in
comparing the latter two figures is that there is more precision in the
estimates using the computed grades, clearly illustrated in Figure 9
,
which compares the SD of the computed approach for each slide with the
actual SD of the graders.
The experiment was developed from questions of grading; however, the
data may provide information pertinent to other areas. For example, how
should images be compressed or coded for telemedicine applications?
Image compression reduces storage or transmission needs but may also be
associated with a loss of information. All the images in our analysis
(and web survey) were stored in a lossless (tagged information file
format [TIFF] file) form, precisely because it is unknown which
attributes of eye images may be discarded without removing critical
information. By better understanding which information is needed
clinically, more effective compression may be developed that minimizes
the loss of critical information at the expense of unimportant image
content—for example, by examining the changes in the image features
fr,
fe as a function of the compression
type. This is similar to previous suggestions17
using
perception to constrain image coding, except that the data are
clinically salient, rather than only perceptually
salient.8
In conclusion, we have shown that computational techniques may be used
to measure the redness characteristics of images of the bulbar
conjunctival areas of eyes. These estimates compared very well with
those derived using clinical grading methods. In addition, the method
we propose has much less variability than that which exists between
clinical graders. In the past 10 years, numerous methods have been
proposed to assign redness scores by computational methods. The
question then might be, how does the procedure developed here advance
these methods? For example, Villumsen et al.8
had strong
correlations between a computational and a grading redness estimate. We
believe that there are a number of reasons why this experiment
describes methods and results that actually make it feasible to use
this technique as a replacement for grading the redness of images.
First, the technology is readily available and inexpensive. Whereas
some previous studies have used rather exotic hardware and software
combinations, the algorithms we used are available to anyone using a
computer running almost every operating system that may be encountered.
Second, a minimum amount of operator intervention is required. This
removes some of the subjectivity in some previous techniques and
further lends itself to automation. And finally, we have shown that
both accuracy and much less variability are present in the automated
technique than in the subjective technique. These factors, we believe,
provide a strong rationale for the adoption of this technique to
replace clinical grading of bulbar redness. Because anterior segment
assessment is much more than just redness evaluation, how to implement
this technique more generally to replace in vivo grading is yet to be
determined.
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Acknowledgements
|
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The authors thank all the observers who visited the web site and
completed the grading task; the following former students for their
dedicated efforts in preparing the survey web site and collecting the
grading data: Janine Cullen, Cynthia Handler, Vikas Nagaraj,
Shannon Nichols, Shane Pounder, and Kimberly Whitear; and the anonymous
reviewers who provided helpful suggestions for improvement of the
manuscript.
|
Footnotes
|
|---|
Submitted for publication April 4, 2001; revised October 1, 2001;
accepted October 18, 2001.
Commercial relationships policy: N.
The publication costs of this article were defrayed in part by page
charge payment. This article must therefore be marked
"advertisement" in accordance with 18 U.S.C. 
1734
solely to indicate this fact.
Corresponding author: Paul Fieguth, Department of Systems Design,
University of Waterloo, Waterloo, Ontario, Canada, N2L-3G1;
pfieguth{at}uwaterloo.ca
|
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Top
Abstract
Introduction
) clinician, (
) the 50% most
consistent clinicians, and (•) the proposed automated system. Except
for grades at the severe end of the scale, where the data were sparse,
the errors of the proposed system are as good as or better than the
50% consensus of a group of 72 graders.