Effect of image quality, color, and format on the measurement of retinal vascular fractal dimension.
ABSTRACT Fractal dimension of retinal vasculature is a global summary measure of retinal vascular network pattern and geometry. This study was conducted to examine the effect of variations in image color, brightness, focus, contrast, and format on the measurement of retinal vascular fractal dimension.
A set of 30 retinal images from the Blue Mountains Eye Study was used for a series of experiments by varying brightness, focus (blur), contrast, and color (color versus monochrome). The original and the modified images were graded for fractal dimension (D(f)) using dedicated retinal imaging software (IRIS-Fractal). A further set of 20 grayscale images was used to compare image format (.jpg versus .tif) with regard to the resultant D(f) and processing time.
The mean D(f) of original images in this sample was 1.454. Compared with the original set of images, variations in brightness, focus, contrast, and color affected the measurements to a small to moderate degree (Pearson correlation coefficient, r, ranged from 0.47 to 0.97). Very dark or blurry images resulted in a substantially lower estimate of D(f). Monochrome images were also consistently associated with lower D(f) compared with that obtained from color images. Using .jpg or .tif image formats did not affect the measurement or the time needed to process and measure D(f).
Variations in image brightness, focus, and contrast can significantly affect the measurement of retinal vascular fractals. Standardization of image parameters and consistent use of either monochrome or color images would reduce measurement noise and enhance the comparability of the results.
- SourceAvailable from: Paul Mitchell[Show abstract] [Hide abstract]
ABSTRACT: Fractal dimensions (FDs) are frequently used for summarizing the complexity of retinal vascular. However, previous techniques on this topic were not zone specific. A new methodology to measure FD of a specific zone in retinal images has been developed and tested as a marker for stroke prediction. Higuchi's fractal dimension was measured in circumferential direction (FDC) with respect to optic disk (OD), in three concentric regions between OD boundary and 1.5 OD diameter from its margin. The significance of its association with future episode of stroke event was tested using the Blue Mountain Eye Study (BMES) database and compared against spectrum fractal dimension (SFD) and box-counting (BC) dimension. Kruskal-Wallis analysis revealed FDC as a better predictor of stroke (H = 5.80, P = 0.016, α = 0.05) compared with SFD (H = 0.51, P = 0.475, α = 0.05) and BC (H = 0.41, P = 0.520, α = 0.05) with overall lower median value for the cases compared to the control group. This work has shown that there is a significant association between zone specific FDC of eye fundus images with future episode of stroke while this difference is not significant when other FD methods are employed.TheScientificWorldJournal. 01/2014; 2014:467462.
- [Show abstract] [Hide abstract]
ABSTRACT: Objective: The purpose of this paper is to determine a quantitative assessment of the human retinal vascular network architecture for patients with diabetic macular edema (DME). Multifractal geometry and lacunarity parameters are used in this study. Materials and methods: A set of 10 segmented and skeletonized human retinal images, corresponding to both normal (five images) and DME states of the retina (five images), from the DRIVE database was analyzed using the Image J software. Statistical analyses were performed using Microsoft Office Excel 2003 and GraphPad InStat software. Results: The human retinal vascular network architecture has a multifractal geometry. The average of generalized dimensions (Dq) for q = 0, 1, 2 of the normal images (segmented versions), is similar to the DME cases (segmented versions). The average of generalized dimensions (Dq) for q = 0, 1 of the normal images (skeletonized versions), is slightly greater than the DME cases (skeletonized versions). However, the average of D2 for the normal images (skeletonized versions) is similar to the DME images. The average of lacunarity parameter, Λ, for the normal images (segmented and skeletonized versions) is slightly lower than the corresponding values for DME images (segmented and skeletonized versions). Conclusion: The multifractal and lacunarity analysis provides a non-invasive predictive complementary tool for an early diagnosis of patients with DME.Current eye research 05/2013; 38(7). · 1.51 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Purpose: Macular diseases may be associated with altered retinal vasculature. We describe and test new software for the measurement of retinal vascular fractal dimension to quantify its complexity at the macula (Mac Df) as compared to around the optic disc (OD Df). Methods: 342 macular-centered and optic disc-centered digital retinal photographs from 171 subjects were randomly selected from a population-based study. Retinal vascular Df was measured by two trained graders using a computer-assisted program (SIVA-FA, version 1.0, National University of Singapore) on macula-centred (Mac Df) and optic disc-centred (OD Df) photographs, to assess inter-grader reliability. Measurements were repeated after two weeks to determine intra-grader reliability. A separate 50 pairs of consecutively repeated images were selected and measured using SIVA-FA to assess intra-session reliability. Reliability analyses were conducted using intra-class correlation coefficients (ICC), and multiple linear regression analyses were performed to compare factors associated with Mac Df and OD Df measurements. Results: The mean (standard deviation) Mac Df and OD Df were 1.453 (0.060) and 1.484 (0.043), respectively and were highly correlated (r=0.70, p<0.001). Intra-grader, inter-grader and intra-session reliability for both Df measures were high (ICCs ranging from 0.88 to 0.99). In multiple regression analyses, age (both β= -0.03, p < 0.001) and hypertension (β= -0.02, p = 0.011; β= -0.02, p = 0.021, respectively) were independently associated with Mac Df and OD Df. Conclusions: The complexity of the retinal vasculature at the macula can be reliably measured and may be a useful tool to study parafoveal vascular networks in macular diseases.Investigative ophthalmology & visual science 02/2014; · 3.43 Impact Factor
Effect of Image Quality, Color, and Format on the
Measurement of Retinal Vascular Fractal Dimension
Alan Wainwright,1Gerald Liew,1George Burlutsky,1Elena Rochtchina,1Yong Ping Zhang,2
Wynne Hsu,3Janice MongLi Lee,4Tien Yin Wong,4,5Paul Mitchell,1and Jie Jin Wang1,4
PURPOSE. Fractal dimension of retinal vasculature is a global
summary measure of retinal vascular network pattern and ge-
ometry. This study was conducted to examine the effect of
variations in image color, brightness, focus, contrast, and for-
mat on the measurement of retinal vascular fractal dimension.
METHODS. A set of 30 retinal images from the Blue Mountains
Eye Study was used for a series of experiments by varying
brightness, focus (blur), contrast, and color (color versus
monochrome). The original and the modified images were
graded for fractal dimension (Df) using dedicated retinal imag-
ing software (IRIS-Fractal). A further set of 20 grayscale images
was used to compare image format (.jpg versus .tif) with regard
to the resultant Dfand processing time.
RESULTS. The mean Dfof original images in this sample was
1.454. Compared with the original set of images, variations in
brightness, focus, contrast, and color affected the measure-
ments to a small to moderate degree (Pearson correlation
coefficient, r, ranged from 0.47 to 0.97). Very dark or blurry
images resulted in a substantially lower estimate of Df. Mono-
chrome images were also consistently associated with lower Df
compared with that obtained from color images. Using .jpg or .tif
image formats did not affect the measurement or the time
needed to process and measure Df.
CONCLUSIONS. Variations in image brightness, focus, and con-
trast can significantly affect the measurement of retinal vascu-
lar fractals. Standardization of image parameters and consistent
use of either monochrome or color images would reduce
measurement noise and enhance the comparability of the
results. (Invest Ophthalmol Vis Sci. 2010;51:5525–5529) DOI:
the health of the circulation.1,2Many reports of studies have
indicated that retinal vessel caliber is associated with an in-
creased risk of systemic outcomes, such as cardiovascular dis-
ease, hypertension, diabetes, and obesity.3–9However, in most
studies to date, limited measures have been examined, such as
retinal vessel caliber in a small, defined region around the optic
disc, which may not adequately reflect changes in the periph-
eral retinal vessels or alterations in vascular branching patterns.
As the retinal vasculature exhibits fractal-like structural char-
acteristics such as self similarity, fractal analysis may offer a
more natural and complete description of retinal vessel struc-
ture and geometry.10–12
Methods of calculating the fractal dimension (Df) of the
retinal vasculature have generally relied on manual tracing of
the retinal vessels to produce a skeletonized binary image from
which the fractal dimension is calculated. Such methods are
slow and prone to subjective error between graders. We have
developed a new software program, International Retinal Im-
aging System—Fractal (IRIS-Fractal) that speeds this process by
automating vessel segmentation. The automation of this step
greatly increases the number of images that can be graded in a
given time and results in minimal between-grader differences.13
Our preliminary results indicate that change in Dfmay be a
sensitive indicator of retinal vascular damage from ocular and
systemic disease processes such as diabetic retinopathy14(in-
creased Df) and elevated blood pressure (decreased Df).13
Thus, the Dfmay be a marker of subtle changes in retinal
vascular architecture, and its measurement from fundus pho-
tographs may be a rapid, noninvasive test for detection of early
vascular disease. However, to maximize the clinical utility, we
require an understanding of how variations in retinal image
parameters influence the measurement of Df.
Successful segmentation of a retinal image requires IRIS-
Fractal to accurately distinguish vessel from nonvessel charac-
teristics under different conditions of brightness, contrast, and
image clarity. These image differences can arise from variations
in photographic technique, pupil dilation, presence of cataract
and other ocular media opacities that can cause blurring of
retinal images and the contrast between retinal vessels and
pigment density of the retinal pigment epithelium (RPE).
Those who scan film-based images to obtain digital images
before processing with IRIS-Fractal may find that the digitiza-
tion process also imparts variations in regard to image resolu-
tion, color (either grayscale or color), and the image formats
selected, which may be based on various methods and levels of
image compression. In addition, the processing time for each
he human retinal vasculature is the only directly accessible
microvasculature and conveys important information on
Research, Westmead Millennium Institute, University of Sydney,
Sydney, Australia; the2School of Electronic and Information Engi-
neering, Ningbo University of Technology, Ningbo, China; the
3School of Computing, National University of Singapore, Singapore;
Melbourne, Australia; and the
Yong Loo Lin School of Medicine, National University of Singapore,
Supported by Australian National Health and Medical Research
Council (NHMRC) Project Grants 153948, 211069, and 302068; Singa-
pore Bioimaging Consortium Grant SBIC RP C-011/2006; Biomedical
Research Council Grant 501/1/25-5; Pfizer Australia Cardiovascular
Lipid Grant 2007; and Diabetes Australia Research Trust (DART) Grant
2007. JJW is funded by a National Health and Medical Research Council
Senior Research Fellowship (2005-2014). IRIS-Fractals was developed
in Singapore funded by the national science agency, A*STAR, which
owns the intellectual property and commercialization rights of this
software. It is currently not commercially available but is freely avail-
able to researchers. WH, JM-LL, and TYW are named inventors of the
patent for the software, but do not have a direct financial interest.
Submitted for publication June 12, 2009; revised December 15,
2009, and March 24, 2010; accepted May 18, 2010.
Disclosure: A. Wainwright, None; G. Liew, None; G. Burlutsky,
None; E. Rochtchina, None; Y.P. Zhang, None; W. Hsu, P; J.M-L.
Lee, P; T.Y. Wong, P; P. Mitchell, None; J.J. Wang, None
Corresponding author: Jie Jin Wang, Centre for Vision Research,
University of Sydney, Westmead Hospital, Hawkesbury Road, West-
mead, NSW, 2145, Australia; email@example.com.
1Department of Ophthalmology, Centre for Vision
4Centre for Eye Research Australia, University of Melbourne,
5Singapore Eye Research Institute,
Clinical and Epidemiologic Research
Investigative Ophthalmology & Visual Science, November 2010, Vol. 51, No. 11
Copyright © Association for Research in Vision and Ophthalmology
image, which may depend on image file size, is important
when grading large image sets for the purpose of screening.
In this study, we used retinal photographs from the Blue
Mountains Eye Study, a large, population-based study of eye
disease conducted in a defined area west of Sydney, Australia,
to assess the effect of these possible limitations on measure-
ment of Dfwith IRIS-Fractal.
We selected a subset of 30 color, positive film photographs of the
retina of the right eye, processed as 35-mm slides from the Blue
Mountains Eye Study baseline participants, conducted from 1992 to
1994. To examine the effect of variation in image brightness, contrast,
and clarity on the segmentation process and thus the final refined
fractal dimension measures, we selected the 30 slides to have good-
quality (defined as having reasonable image brightness and clarity on
visual inspection by the grader and to produce gradable vessel traces,
according to protocol), disc-centered images from normotensive sub-
jects who were free of known systemic disease. The 30 selected slides
were digitized to produce .tif monochrome (grayscale) images (size ?
9.6 Mb, 3888 ? 2595 pixels) with a slide scanner (CanoScan FS2710;
Canon Corp., Tokyo, Japan). IRIS-Fractal was used to process each
image through to the image-cropping stage (Fig. 1).13The cropping of
the image involved drawing a circular mask with a radius of 3.5 optic
disc radii centered on the optic disc. This process is intended to
provide a consistent area of measurement across individuals, while
minimizing loss of focus and image artifact at the edge of the photo-
graphic field.13After they were cropped, the images were saved as a
bitmap. Saving the cropped images allowed identical images to be used
for each subsequent image set, formed by manipulation of brightness,
blur, and contrast. It therefore ensured that an identical area was
measured for each image under the various conditions studied. We
then performed a series of experiments.
Experiment 1: Image Brightness Variation
The pixel density histogram of each cropped grayscale image was
examined (Fig. 2). As there were no pure white pixels in any of the
grayscale images and most of the numerous black pixels in the pixel
density histogram were likely to have come from the black border that
results from cropping, we examined the substantive middle section of
the histogram and recorded the highest and lowest pixel values. From
these data, it was determined that a maximum shift of 40-pixel bright-
ness values could be made in both directions, for every image, without
information loss. Thus, the position of the histogram, with respect to
light and dark, was changed but without altering its shape. Eight
brightness-variation image sets (?40, ?30, ?20, ?10, ?10, ?20, ?30,
and ?40) were created (Photoshop CS2; Adobe Systems, San Jose, CA).
These image sets were graded with IRIS-Fractal and compared to the
Experiment 2: Image Focus Variation
The Lens Blur filter in Photoshop was used to produce three image sets
that varied in image clarity (Fig. 3). The settings used were “hexagonal
iris,” with a blur radius of 10, 20, or 30. The images produced were
graded and compared with the reference set.
Experiment 3: Image Contrast Variation
A maximum contrast–expansion grayscale image set was obtained,
applying a 0.1% clip of the upper and lower pixels in each image (Fig. 4).
This process was completed in Photoshop via the use of the Auto
Levels function and standard settings. This setting was chosen over the
Auto Contrast function, which ignores the top and bottom 0.5%, to
cropped and the raw vessel tracing is produced. The raw trace is graded by a trained grader and the Dffor the refined image is calculated.
Pictorial view of the IRIS-Fractal grading process. The original color slide is scanned as a grayscale image. The grayscale image is then
adjustments were made in both the positive and negative directions (top). The resulting refined skeletonized line tracing is shown below each
brightness-adjusted image (middle), along with its associated Dfmeasure. The pixel density histogram for each image is also shown (bottom).
This process was repeated for each of the 30 images in the experimental dataset.
Brightness variations. This series shows the effect of brightness adjustments applied to a single original image. Stepwise, four 10-pixel
5526 Wainwright et al.
IOVS, November 2010, Vol. 51, No. 11
retain as much of the information contained in the image as possible
while still increasing the relative difference between gray values in the
Experiment 4: Image Color Variation
We determined whether there was a difference between the Dfob-
tained from color and grayscale images by scanning the original 30
color slides as color .tif images. Both this new color .tif image set and
the original grayscale .tif image set were graded according to the
standard IRIS-Fractal protocol, and the resulting means were com-
pared. No image manipulation was conducted for this analysis.
Experiment 5: Image Format Variation
The effect of image format on Dfmeasurement was studied by com-
paring the fractal dimension from an image set scanned in .jpg with
those scanned in .tif. An additional 20 color slides were chosen from
the baseline BMES population, and each image was scanned, in gray-
scale, as both .jpg (average image size, 0.49 Mb) and .tif (9.61 Mb). This
method gave an approximately 20:1 compression over the standard
image and would be considered high (quality grade, 8) .jpg compres-
sion in Photoshop. The standard IRIS-Fractal grading process was then
applied to each image set and the results compared.
As the amount of time spent on grading an image is important, we also
measured the mean time (in seconds) necessary for IRIS-Fractal to
calculate the raw fractal dimension and produce the initial ungraded
vessel trace. This comparison was conducted by using the 20-image
grayscale .jpg and .tif image sets that were used for examining the
effect of image format.
As a continued check of intragrader reliability, the original gray-
scale 30-image set was regraded by the same grader and the results
compared with those obtained at the beginning of the study.
The mean Dfobtained from each manipulated image set was compared
with the mean Dfobtained from the unaltered, original cropped image
set. The paired Student’s t-test was used to compare the mean Dfof the
reference image set and each of the brightness-, blur-, and contrast-
adjusted image sets. The paired Student’s t-test was also used for
comparisons of grayscale versus color images, the .tif versus .jpg image
sets, and for the analysis of processing time for the .tif and .jpg image
sets. The Pearson correlation was also calculated for all comparisons,
along with the Cohen d15as a measure of effect size (defined as the
difference in means divided by the standard deviation of the original
unaltered image set).
d ? ?mean1? mean2?/SD1
For comparisons that were not between pre- and postalteration data
sets (color versus grayscale, .tif versus .jpg, and processing time) the
square root of the pooled variance was used instead of the standard
d ? ?mean1? mean2?/ ????1
P ? 0.05 was considered significant. As statistical significance does not
necessarily equal practical or clinical significance, effect sizes were
used to describe the magnitude of the differences relative to the
standard deviation of the measures from the original images. Effect size
was graded as suggested by Cohen, with effects considered to be small
at 0.2, moderate at 0.5, and large at 0.8 or greater.15
The mean Dfof the reference image set was at 1.4542, with a
mean median pixel value of 99.9. Table 1 shows the results of
experiments 1 to 3. Increasing brightness (experiment 1) was
not found to significantly affect the measurement of Df. A
brightness increase of 40 resulted in a mean Dfof 1.4541,
which was not significantly different from the mean Dfof the
reference set (1.4542; P ? 0.60, d ? 0.01). However decreas-
ing brightness by 4 units significantly lowered the measure-
ment of Df(mean Df, 1.4113; P ? 0.0001, d ? 3.14; Table 1,
Increasing the amount of blur in an image (defocusing,
experiment 2) lowered the measured Dfsignificantly (mean Df,
1.4118; P ? 0.0001, d ? 3.11; Table 1) In experiment 3,
applying a maximum contrast operation to each image in the
set increased the measured Df. The increase was small (mean Df,
1.4570; P ? 0.005), as was the effect size (d ? 0.21; Table 1).
There was a significant difference in the Dfgained from a
color image set (experiment 4) compared with that gained
from the same image set but scanned and processed in gray-
scale (1.4692 vs. 1.4562; P ? 0.0001, d ? 0.95; Table 2).
In experiment 5, image format (.tif versus .jpg) for grayscale
images did not affect the measured Df(Table 2).
Finally, image format also did not affect the processing time
for each image, with production of the initial vessel trace
0.1% clip of maximum and minimum pixel values. The pixel density
histogram shows how the pixel values in the image are spread out to
increase the difference between them and thus make small differences
more obvious. Dfis given below the images. This adjustment was
performed for each of the 30 images in the experimental dataset.
Contrast-stretch. A contrast-stretch operation involving a
applied to the original image. The resultant skeletonized line tracing
and associated Dfare shown for each condition. Note loss of smaller
vessel traces as blur increases. This sequence of image blurring was
repeated for each of the 30 images in the experimental dataset.
Focus (blur) variations. Three increasing levels of blur were
IOVS, November 2010, Vol. 51, No. 11
Effect of Image Quality on Measurement of Fractal Dimension5527
taking a mean time of 90.15 seconds for the .tif image set
versus 90.14 seconds for the .jpg image set (Table 3). Intra-
grader reliability was high (0.95), which agrees with the find-
ings of Liew et al.13
Fractal analysis of retinal vessels may convey valuable informa-
tion on microvascular structure that is absent from alternative
measures such as retinal vessel caliber.2,13,14To fully use this
potential, we examined how variations in retinal image quality
may influence the measurement of Df. The simulated degrada-
tion levels are comparable with the quality range of images in
real life, particularly images taken from older persons who may
have various pupil sizes and levels of cataract and other ocular
media opacities. In five experiments, we attempted to recreate,
via the use of software, the typical variations due either to
differences in photographic technique (e.g., amount of flash
used, the ability of the photographer to achieve correct focus,
and the exposure) or to anatomic and physiological features
that may not be related to the structure and complexity of the
retinal vasculature (e.g., retinal pigment epithelial cell pigment
density, pupil dilation in response to phenylephrine and/or
tropicamide, and the presence of cataract or other ocular
In this study, we found that variations in image brightness,
clarity, and contrast all affected the ability of IRIS-Fractal to
accurately segment a digitized image of the retinal vasculature
and, thereby, the measurements of Dfproduced by IRIS-Fractal.
Dfassessment was thus also sensitive to these image variations.
Effect sizes for most of these image variations (contrast expan-
sion, small decrease in image brightness) were low to moder-
ate (Cohen d, 0.21–0.46). Increased blur and decreased bright-
ness both impaired the ability of IRIS-Fractal to accurately
segment a retinal image, resulting in spuriously low Df. How-
ever, these reductions in measured Dfwere generally of small
magnitude, except for the lowest range of pixel brightness
values and greatest amounts of blur (Table 1). Conversely, an
increase in image brightness was not associated with any sig-
nificant change in the reported Dfmeasures, nor was a small
amount of image blurring. Moderately improving contrast im-
proved detection of vessels and measurement of Dfby a small
but significant amount (Dfdifference, 0.0028; P ? 0.005, d ?
0.21), considering that the images studied were all reasonable
quality to begin with. The increase in measured Dfseen after
contrast expansion is not due to the change in brightness of
the images, as the median pixel value of the contrast expansion
set, 137.3, is within the range of that produced by the bright-
ness increases that we examined and which were found to
have no effect. Similarly, the effect on Dfcaused by the blur-
ring of an image is not related to any change in brightness, as
each image-blur set had a median pixel value similar that of to
the others and also to the reference set.
We found that IRIS-Fractal produced different measures of
Dfwhen analyzing color as opposed to color-scanned-as-gray-
scale images, with the color image generally giving higher Df.
This effect size was large (average Dfdifference, 0.013; P ?
0.0001, d ? 0.95). From manual inspection, color images
appeared to generate a more faithful tracing of the retinal
vasculature and were thus less susceptible to false negatives
(i.e., failure to detect and segment vessels), because the green
channel of color images provides the greatest contrast and
details of the images. This finding suggests that, preferen-
TABLE 2. Results of Image Formats and Measure
of Intragrader Reliability
Image FormatMean Df
The image format comparisons were .tif versus .jpg and color
versus grayscale (n ? 20 for .jpg vs. tif, n ? 30 for other).
* d ? (mean1? mean2)/?[(?1
TABLE 3. Processing Time for Grayscale .tif and .jpg Images
n ? 20 for each set. r ? Pearson correlation coefficient.
* d ? (mean1? mean2)/?[(?1
TABLE 1. Effects of Changes in Brightness, Focus, and Contrast on Df
Experiment 1: brightness adjustment
Experiment 2: image blurring
Lens blur r10
Lens blur r20
Lens blur r30
Experiment 3: contrast expansion
n ? 30 for each set. Brightness ?x, the amount the reference image was lightened or darkened.
* d ? (mean1? mean2)/standard deviation. r ? Pearson correlation coefficient.
5528 Wainwright et al.
IOVS, November 2010, Vol. 51, No. 11
tially, color slides should be scanned and graded in color
rather than in grayscale when using IRIS-Fractal. Although
grayscale-scanned-from-color images may provide useful in-
formation on associations between retinal vessel complexity
and various physiological markers of interest (e.g., presence of
retinopathy lesions), our results suggest that the Dfmeasure-
ments gained from this process may not be directly compara-
ble to those gained from color grading of the same images.
Brightness thresholds below the default used in this study
appear to adversely affect the accuracy of IRIS-Fractal in tracing
retinal vessels, and could thus result in spuriously lower mea-
surements of Df. As brightness variation across an image is
common in retinal photographs, some form of image postpro-
cessing to increase the uniformity of brightness across an
image (e.g., shading correction, antivignetting, brightness ad-
justments, contrast expansion, or combinations of these and
others) may help improve the accuracy of Dfmeasures. Fur-
ther, images without extreme blur or defocus and with con-
trast similar to those in this study will provide consistent and
reasonably accurate measurements of Df..
IRIS-Fractal is not sensitive to the format of the image,
allowing moderate compression of grayscale images (.jpg) to
be used without unduly affecting the results. The size of the
image file was also unrelated to the time taken to analyze
retinal vascular structure. This finding supports the use of .jpg
format in image storage, for measurement of retinal vascular
Our study has several strengths including the use of actual
epidemiologic study images and manipulation through a range
of image degradations. Compared with the distribution of Dfin
the whole Blue Mountains Eye Study (BMES) population (mean
Df, 1.441, SD 0.024; interquartile range, 1.428–1.457; Mitchell
P, et al. IOVS 2008;49:ARVO E-Abstract 603; personal commu-
nication), the changes from most artificial image degradations
were ?1 SD and may be considered of less concern. However
when the brightness was reduced by 40 units, the change in Df
(?0.043) was more than the interquartile range. Although this
is a consequential difference, it is likely to occur only in a small
number of images (being the maximum decrease in brightness
that we examined). As our work focused on IRIS-Fractal and
the segmentation algorithms that we used, it remains unclear
how our results apply to other software with different algo-
rithms or segmentation methods. Nonetheless, we believe that
developers of other image-processing software should be
aware of these aspects, particularly for subtle measurements. It
also remains unclear how software-induced degradation of
images is related to real-life image degradation due to photo-
graphic (e.g., angle of photography and defocus) and patient-
related (e.g., cataract, pupil size and ethnicity) factors. We did
not evaluate whether changes in measurements due to image
degradation affect the clinical utility of this software, as the
differences in measurements were of the same order of mag-
nitude as the differences between normal control subjects and
patients with different disease states (e.g., hypertension and
diabetic retinopathy) reported in other studies.13,14This pos-
sible drawback may limit the feasibility of using the current
software in comparative studies (when image quality is not
similar) or in prospective studies in which Dfis used as a risk
factor in the diagnosis or follow-up of patients with early subtle
vasculopathy (when image quality is different at different time
points). These limitations must be addressed before the cur-
rent fractal software can be applied to clinical settings.
In conclusion, our results suggest that variation in bright-
ness has only a small effect on the ability of IRIS-Fractal to
measure Df, provided the lowest ranges of brightness are
avoided (i.e., keeping the median pixel value of the cropped
images above 90). A small amount of image blurring is also well
tolerated by IRIS-Fractal; however, an increase in image blur
beyond this relatively small amount was associated with signif-
icantly lower Dfmeasures. Moderately increasing contrast has
a small but positive impact on the ability of this program to
accurately segment images and thereby measure Df. A signifi-
cant difference was seen in measured Dfbetween color and
color-scanned-as-grayscale images, with color images generally
providing higher measures of Df. Our study has raised image
quality as an issue that may also be applicable to other auto-
mated vessel-imaging programs using the same or different
segmentation methods. These differences may have to be ac-
counted for when measuring Dfof the retinal vasculature and
examining associations with ocular and systemic disease. Fur-
ther work to determine optimal parameters of image bright-
ness, focus, and contrast for measurement of retinal fractals
may allow the development of these measurements as novel
tests for vascular disease in the eye and elsewhere in the body.
1. Wong TY, Mitchell P. Hypertensive retinopathy. N Engl J Med.
2. Liew G, Wang JJ, Mitchell P, Wong TY. Retinal vascular imaging: a
new tool in microvascular disease research. Circ Cardiovasc Im-
aging Sep. 2008;1:156–161.
3. Wong TY, Mitchell P. The eye in hypertension. Lancet. 2007;369:
4. Sun C, Wang JJ, Mackey DA, Wong TY. Retinal vascular caliber:
systemic, environmental, and genetic associations. Surv Ophthal-
5. Wang JJ, Liew G, Klein R, et al. Retinal vessel diameter and
cardiovascular mortality: pooled data analysis from two older pop-
ulations. Eur Heart J. 2007;28:1984–1992.
6. Wong TY, Klein R, Sharrett AR, et al. Retinal arteriolar narrowing
and risk of coronary heart disease in men and women. The Ath-
erosclerosis Risk in Communities Study. JAMA. 2002;287:1153–
7. Wang JJ, Rochtchina E, Liew G, et al. The long-term relation among
retinal arteriolar narrowing, blood pressure, and incident severe
hypertension. Am J Epidemiol. 2008;168:80–88.
8. Ikram MK, de Jong FJ, Bos MJ, et al. Retinal vessel diameters and
risk of stroke: the Rotterdam Study. Neurology 2006;66:1339–
9. Wong TY, Islam FM, Klein R, et al. Retinal vascular caliber, cardio-
vascular risk factors, and inflammation: the multi-ethnic study of
atherosclerosis (MESA). Invest Ophthalmol Vis Sci. 2006;47:2341–
10. Stosic T, Stosic BD. Multifractal analysis of human retinal vessels.
IEEE Trans Med Imaging. 2006;25:1101–1107.
11. Masters BR. Fractal analysis of the vascular tree in the human
retina. Annu Rev Biomed Eng. 2004;6:427–452.
12. Avakian A, Kalina RE, Sage EH, et al. Fractal analysis of region-
based vascular change in the normal and non-proliferative diabetic
retina. Curr Eye Res. 2002;24:274–280.
13. Liew G, Wang JJ, Cheung N, et al. The retinal vasculature as a
fractal: methodology, reliability, and relationship to blood pres-
sure. Ophthalmology. 2008;115:1951–1956.
14. Cheung N, Donaghue KC, Liew G, et al. Quantitative assessment of
early diabetic retinopathy using fractal analysis. Diabetes Care.
15. Cohen J. A power primer. Psychol Bull. 1992;112:155–159.
IOVS, November 2010, Vol. 51, No. 11
Effect of Image Quality on Measurement of Fractal Dimension 5529