J. Biomedical Science and Engineering, 2011, 4, 657-665
doi:10.4236/jbise.2011.410082 Published Online October 2011 (http://www.SciRP.org/journal/jbise/
JBiSE
).
Published Online October 2011 in SciRes. http://www.scirp.org/journal/JBiSE
Investigation of changes in thickness and reflectivity from
layered retinal structures of healthy and diabetic eyes with
optical coherence tomography
Wei Gao1, Erika Tátrai2, Veronika Ölvedy2, Boglárka Varga2, Lenke Laurik2, Anikó Somogyi3,
Gábor Márk Somfai2, Delia Cabrera DeBuc1
1Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, USA;
2Semmelweis University, Department of Ophthalmology, Budapest, Hungary;
3Semmelweis University, 2nd Department of Internal Medicine, Budapest, Hungary.
Received 2 June 2011; revised 15 September 2011; accepted 26 September 2011.
ABSTRACT
OCT is usually employed for the measurement of
retinal thickness. However, coherent reflected light
carries more information characterizing the optical
properties of tissue. Therefore, optical property
changes may provide further information regarding
cellular layers and early damage in ocular diseases.
We investigated the possibility of OCT in detecting
changes in the optical backscattered signal from lay-
ered retinal structures. OCT images were obtained
from diabetic patients without retinopathy (DM, n =
38 eyes) or mild diabetic retinopathy (MDR, n = 43
eyes) and normal healthy subjects (n = 74 eyes). The
thickness and reflectivity of various layered struc-
tures were assessed using a custom-built algorithm.
In addition, we evaluated the usefulness of quantify-
ing the reflectivity of layered structures in the detec-
tion of retinal damage. Generalized estimating equa-
tions considering within-subject inter-eye relations
were used to test for differences between the groups.
A modified p value of <0.001 was considered statisti-
cally significant. Receiver operating characteristic
(ROC) curves were constructed to describe the abil-
ity of each parameter to discriminate between the
eyes of DM, MDR and healthy eyes. Thickness values
of the GCL + IPL and OPL showed a significant de-
crease in the MDR eyes compared to controls. Sig-
nificant decreases of total reflectance average values
were observed in all layers in the MDR eyes com-
pared with controls. The highest AUROC values es-
timated for the total reflectance were observed for
the GCL + IPL, OPL and OS when comparing MDR
eyes with controls. Total reflectance showed a better
discriminating power between the MDR eyes and
healthy eyes compared to thickness values. Our re-
sults suggest that the optical properties of the in-
traretinal layers may provide useful information to
differentiate pathological from healthy eyes. Further
research is warranted to determine how this ap-
proach may be used to improve diagnosis of early
retinal neurodegeneration.
Keywords: Optical Coherence Tomography; Rerina;
Diabetic Retinopathy; Segmentation; Image Processing
1. INTRODUCTION
Optical coherence tomography (OCT) is an optical im-
aging technique that has high axial resolution and high
dynamic range by the use of a broadband light source
and heterodyne detection technique [1]. Along with im-
aging, OCT can also be used for quantitative analysis of
tissue optical properties as the OCT signal depends on
the total attenuation and backscattering coefficients [2].
This technique provides information on the optical
properties of microstructures such as reflectance, scat-
tering coefficient, absorption coefficient, refractive index
and birefringence. OCT has been shown to be appropri-
ate for non-invasive two-dimensional imaging of micro-
structures underneath the tissue surface [3,4]. In addition,
OCT has been used to measure optical properties of tis-
sues, to derive spectroscopic information from tissue
phantoms and to investigate the optical clearing of soft
tissue and whole blood [5-8]. Hammer et al. investigated
the optical scattering of four posterior eye segments
from bovine/porcine samples [9,10].
From the clinical point of view, OCT has also been
used to diagnose and follow-up ocular disorders. For
example, optic nerve head disorders and macular dis-

W. Gao et al. / J. Biomedical Science and Engineering 4 (2011) 657-665 658
eases involving both inner and outer cellular layers have
been extensively investigated with this technology [11].
OCT is usually employed for the measurement of retinal
thickness. Particularly, the quantification of structural
changes of the various cellular layers of the retina with
OCT has helped to assess treatment efficacy and identify
potential markers for monitoring the disease progression.
However, coherent reflected light carries more informa-
tion characterizing the optical properties of tissue. The-
refore, the changes in tissue optical properties may pro-
vide further information regarding cellular layers and
early damage in ocular diseases.
It is known that diabetes leads to a thinning of the
macula preceding the onset of severe diabetic retinopa-
thy, which is most possibly attributed to neurodegenera-
tion [12]. We have shown previously that the thinning of
the retina is due to a loss of the inner retina, namely the
ganglion cells [13] which is in accordance with the find-
ings of other [14].
Our aim was to investigate the possibility of OCT to
detect changes in the optical backscattered signal (i.e.
reflectivity) from layered retinal structures. OCT images
were obtained from diabetic and normal healthy subjects
and the thickness and reflectivity of various layered
structures were assessed using a custom-built algorithm.
In addition, we evaluated the usefulness of quantifying
the reflectivity of layered structures in the detection of
retinal damage.
2. MATERIALS AND METHODOLOGY
2.1. Data Collection
The study conducted in this paper was approved by the
Institutional Review Boards in our institutions. The re-
search adhered to the tenets set forth in the declaration of
Helsinki. Informed consent was obtained from each
subject. OCT examination was performed in healthy and
diabetic eyes with and without retinopathy. A total of 74
healthy eyes (34 ± 12 yr, 52 female, 22 male), 38 eyes
with type 1 diabetes mellitus (DM) with no retinopathy
(35 ± 10 yr, 20 female, 18 male) and 43 eyes with mild
diabetic retinopathy (MDR, 43 ± 17 yr, 21 female, 22
male) on biomicroscopy were included in the study (see
Table 1).
2.2. OCT System and Measurements
The OCT system (Stratus OCT, Carl Zeiss Meditec,
Dublin, California) used in this study employs a broad-
band light source, delivering an output power of 1 mW
at the central wavelength of 820 nm with a bandwidth of
25 nm. The light source yields 12 µm axial resolution in
free space that determines the imaging axial resolution
of the system. A cross-sectional image is achieved by the
combination of axial reflectance while the sample is
Table 1. Characteristics of study participants. Abbreviations:
SD = standard deviation.
Characteristic Controls DM MDR
Number of
Participants 41 29 29
Number of Eyes 74 38 43
Age
(years, mean ± SD) 34 ± 12 35 ± 10 43 ± 17
Female, N
(% total eyes) 52 (70%) 20 (53%) 21 (49%)
Race
(% Caucasian) 100 100 91
Hemoglobin
A1c level (%) - 7.20 ± 0.90 8.51 ± 1.76
DM duration
(years, mean ± SD) - 13 ± 5 22 ± 10
BCVA 1.0 ± 0.00 1.0 ± 0.00 0.97 ± 0.06
scanned laterally. All Stratus OCT’ study cases were
obtained using the Macular Thickness Map (MTM) pro-
tocol. This protocol consists of six radial scan lines cen-
tered on the fovea, each having a 6mm transverse length.
In order to obtain the best image quality, focusing and
optimization settings were controlled and scans were
accepted only if the signal strength (SS) was above 6
(preferably 9 - 10) [15]. Scans with foveal decentration
(i.e. with center point thickness SD > 10%) were repeated.
2.3. OCT Image and Data Analysis
Macular radial line scans of the retina for each case were
exported with the export feature available in the Stratus
OCT device and analyzed using a custom-built software
for OCT image analysis [16,17]. Segmentation errors
were manually corrected using the manual correction
tool provided by OCTRIMA. The OCTRIMA method-
ology essentially provides dual functionality by com-
bining image enhancement and denoising of OCT im-
ages along with automatic segmentation of the various
cellular layers of the retina. Moreover, OCTRIMA has
the capability to perform calculations based on measured
values of corrected thickness and reflectance of the
various cellular layers of the retina and the whole mac-
ula. The OCTRIMA software enables the segmentation
of 7 cellular layers of the retina on OCT images based
on their optical densities: the retinal nerve fiber layer
(RNFL), the ganglion cell and inner plexiform layer
complex (GCL + IPL), the inner nuclear layer (INL), the
outer plexiform layer (OPL), the outer nuclear layer and
inner photoreceptor segment (ONL + IS), outer photore-
ceptor segment (OS) and retinal pigment epithelium
(RPE) (see Figure 1). We have previously shown a high
reliability and reproducibility of OCTRIMA software
using Stratus OCT data from normal healthy eyes [17,
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W. Gao et al. / J. Biomedical Science and Engineering 4 (2011) 657-665 659
18]. As in some Fourier-domain OCT (FD-OCT) sys-
tems, OCTRIMA facilitates the total retinal thickness
calculations between the ILM and the inner boundary of
the second hyperreflective band, which has been attrib-
uted to the outer segment/retinal pigment epithelium
(OS/RPE) junction in agreement with histological stud-
ies [19-21].
Lateral coordinates of the blood vessel shadows were
first extracted by using a blood vessel shadowgram
technique [22]. Then, these shadows were removed in
each OCT image (see Figure 2) before calculating re-
flectivity values. Average values of total reflectance and
thickness per intraretinal layer were calculated. Total
reflectance values included average values of relative
internal reflectivity (NRIR: reflectivity normalized to the
maximum value within the whole retina) and reflectivity
with normalization to the RPE reflectance (NRPE). Total
reflectance values were converted to decibels (dB = 10 ×
log10 [TR]). Generalized estimating equations consider-
ing within-subject inter-eye relations were used to test
for differences between the groups. A modified p value
of <0.001 was considered statistically significant. Re-
ceiver operating characteristic (ROC) curves were con-
structed to describe the ability of each parameter to dis-
criminate between the eyes of diabetic patients without
retinopathy with diabetic patients with retinopathy and
healthy eyes. It is worth to note that an area under curve
(AUROC) of 1.0 indicates perfect discrimination, while
an AUROC of 0.5 indicates no discrimination. For the
statistical analyses SPSS Statistics 17.0 software was
used.
3. RESULTS
Thickness values of the GCL + IPL and OPL showed a
significant decrease in the MDR eyes compared to con-
trols (see Table 2). Average values in other layers (except
(a)
(b)
Figure 1. Macular image segmentation using OCTRIMA. (a) The image of a healthy macula scanned by Stratus OCT.
(b) The same OCT scan processed with OCTRIMA. Abbreviations: Ch, choroid; GCL + IPL, ganglion cell layer and
inner plexiform layer complex; INL, inner nuclear layer; ONL + IS, combined outer nuclear layer and inner segment
of photoreceptors; OS, outer segment of photoreceptors; OPL, outer plexiform layer; RNFL, retinal nerve fiber layer;
RPE, retinal pigment epithelial layer; V, vitreous. Note that OCTRIMA measures the thickness of the total retina be-
tween the inner limiting membrane and the inner boundary of the photoreceptor outer segment/RPE junction. The
thickness of the combined ONL + IS structure is measured between the outer boundary of OPL and the inner bound-
ary of the photoreceptor outer segment/RPE junction.
(a) (b) (c)
Figure 2. Image denoising and segmentation. (a) OCT raw image. (b) OCT image showing segmentation results after
removal of speckle noise. (c) OCT filtered image showing the location of the extracted blood vessel boundaries using
the shawdogram technique.
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W. Gao et al. / J. Biomedical Science and Engineering 4 (2011) 657-665 660
Table 2. Distribution statistics of thickness (mean ± SD) values
by study group.
Thickness (µm) Controls DM MDR
RNFL 42.02 ± 2.35 41.19 ± 2.45 41.39 ± 3.28
GCL + IPL 78.30 ± 4.61 75.41 ± 5.70 71.80 ± 8.89‡
INL 35.02 ± 1.78 35.74 ± 2.29 35.05 ± 2.99
OPL 41.30 ± 2.71 39.88 ± 5.19 36.07 ± 3.77‡
ONL + IS 86.41 ± 5.61 85.55 ± 8.12 88.40 ± 9.19
OS 16.30 ± 3.27 17.98 ± 2.96 14.60 ± 2.07
RPE 12.71 ± 1.49 13.78 ± 1.34 12.76 ± 1.18
‡p < 0.001 between Controls and MDR (Generalized estimating equations).
the RPE and ONL + IS) showed a tendency towards
thinning without reaching significance as compared to
DM and normal healthy eyes. Significant decreases of
total reflectance average values using both NRIR and
NRPE normalizations were observed in all layers in the
MDR eyes compared with controls (except RPE, see
Tables 3 and 4).
The ROC analysis was performed for thickness and
total reflectance (NRIR & NRPE) average values of each
intraretinal layer (see Figure 3). The AUROC values are
shown in Tables 5 by study groups. Detailed ROC
analysis results for variables that showed significant
difference between the groups are also shown in Tables
6 and 7. The highest values estimated for the total re-
flectance (NRIR & NRPE) were observed for GCL +
IPL, OPL and OS when comparing MDR with healthy
normal eyes. The highest values estimated for the thick-
ness were observed for GCL + IPL and OPL when com-
paring MDR with healthy normal eyes. Total reflectance
showed a better discriminating power between the MDR
eyes and healthy eyes.
4. DISCUSSION
OCT is usually employed for the measurement of retinal
thickness. However, coherent reflected light carries more
information characterizing the optical properties of tis-
sue. Therefore, optical property changes may provide
further information regarding cellular layers and early
damage in ocular diseases. We investigated the possibil-
ity of OCT in detecting early changes in the optical
backscattered signal from layered retinal structures in
diabetic eyes. In this study, the total reflectance dis-
played the most powerful diagnostic utility for detecting
early changes in the diabetic retina.
Quantitative OCT-based measures have become an
essential part of diabetic macular assessment and man-
agement during the last years. OCT images can be used
to understand the early histological changes of the macula
Table 3. Distribution statistics of total reflectance (NRIR,
mean ± SD) values by study group.
Total Reflectance
(dB, NRIR) Controls DM MDR
RNFL 19.13 ± 1.23 18.54 ± 1.15 17.55 ± 1.46‡
GCL + IPL 20.21 ± 1.75 19.66 ± 1.47 17.88 ± 1.73‡
INL 10.08 ± 2.14 10.15 ± 1.87 8.46 ± 1.91
OPL 13.33 ± 2.19 13.07 ± 2.46 10.62 ± 1.74‡
ONL+IS 14.93 ± 2.23 14.54 ± 1.81 13.34 ± 2.13
OS 13.08 ± 1.32 13.52 ± 0.98 11.64 ± 0.94‡
RPE 13.38 ± 0.92 13.82 ± 0.92 13.01 ± 0.78
‡p < 0.001 between Controls and MDR (Generalized estimating equations).
Table 4. Distribution statistics of total reflectance (NRPE)
values by study group.
Total Reflectance
(dB, NRPE) Controls DM MDR
RNFL 22.58 ± 1.12 22.06 ± 1.02 21.42 ± 1.44‡
GCL + IPL 23.44 ± 1.50 22.97 ± 1.26 21.45 ± 1.66
INL 13.27 ± 1.87 13.44 ± 1.57 12.00 ± 1.70
OPL 16.25 ± 1.94 16.14 ± 2.12 13.93 ± 1.57‡
ONL + IS 17.86 ± 1.99 17.59 ± 1.56 16.60 ± 2.03
OS 16.11 ± 1.12 16.64 ± 0.98 15.00 ± 0.95‡
RPE 16.34 ± 0.76 16.85 ± 0.70 16.23 ± 0.57
‡p < 0.001 between Controls and MDR (Generalized estimating equations).
in diabetes by comparing the thickness and reflectance
measurements of the various cellular layers of the retina
in diabetic patients with minimal DR with the thickness
and reflectance measurements in normal healthy subjects
and diabetic patients who have no retinopathy. This par-
ticular comparison is especially important in the early
stages of DR when the structural changes are not yet
evident with slit-lamp biomicroscopy or angiographi-
cally [15,20]. To date, all studies reporting retinal
changes associated to diabetes have only used thickness
measurements [12]. These studies have generated con-
tradictory results and conclusions. Particularly, accord-
ing to the first reports with the use of OCT it seemed that
thickening of the retina may be an early sign of diabetic
changes in eyes with no significant macular edema [23].
For example, in the study by Schaudig et al. (2000) an
increased retinal thickness of the macula in the superior
nasal quadrant was observed in patients with DR as
compared to patients without DR and controls [24].
Oshitari et al. (2008) showed that the macula was thicker
and RNFL was thinner at the early stages of DR, and
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(a) (b)
(c) (d)
Figure 3. Receiver operating characteristic (ROC) curves for the detection of early diabetic retinopathy using thickness and total
reflectance (NRPE) as predictor variables. (a) Thickness (MDR vs. Controls); (b) Total reflectance (NRPE, MDR vs. Controls); (c)
Thickness (MDR vs. DM); (d) Total reflectance (NRPE, MDR vs. DM). Areas under the ROC curves for total reflectance (NRPE)
were significantly greater than that for the thickness analysis (P ≤ 0.05).
Table 5. AUROC values of best diagnostic parameters by study group.
Intraretinal Layer MDR vs. Controls MDR vs. DM DM vs. Controls
Thickness (μm)
RNFL 0.592 0.526 0.576
GCL + IPL‡ 0.739* 0.622 0.65
INL 0.505 0.574 0.407
OPL‡ 0.867** 0.718* 0.603
ONL+IS 0.414 0.424 0.501
OS 0.658 0.835** 0.333
RPE 0.49 0.717* 0.313
Total reflectance (dB, NRPE)
RNFL‡ 0.747* 0.646 0.639
GCL + IPL‡ 0.821** 0.772** 0.626
INL‡ 0.71* 0.741* 0.49
OPL‡ 0.817** 0.795** 0.519
ONL + IS‡ 0.698* 0.687* 0.553
OS‡ 0.776** 0.892** 0.361
RPE 0.546 0.75* 0.328
* 0.7≤ AUROC≤0.8, **0.8 ≤ AUROC, ‡p < 0.001 between Controls and MDR (ANOVA followed by Newman-Keuls post hoc analysis).
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Table 6. Cutoff values derived from the ROC analyses for variables that showed significant difference between the MDR group and
Controls.
Asymptotic 95% CI
Intraretinal Layer AUROC
Lower bound Upper bound
Cut off point Sensitivity Specificity
Thickness (μm)
RNFL 0.592 0.477 0.707 41.0 66.2% 58.1%
GCL + IPL 0.739 0.637 0.841 75.4 74.3% 65.1%
OPL 0.867 0.786 0.949 38.9 83.8% 83.7%
OS 0.658 0.558 0.757 14.6 67.6% 58.1%
Total reflectance (dB, NRPE)
RNFL 0.747 0.654 0.841 22.1 73.0% 67.4%
GCL + IPL 0.821 0.741 0.901 22.7 77.0% 74.4%
OPL 0.817 0.739 0.894 15 77.0% 76.7%
OS 0.776 0.69 0.861 15.5 71.6% 69.8%
Table 7. Cutoff values derived from the ROC analyses for variables that showed significant difference between the MDR and DM
groups.
Asymptotic 95% CI
Intraretinal Layer AUROC
Lower bound Upper bound
Cut off point Sensitivity Specificity
Thickness (μm)
RNFL 0.526 0.398 0.654 41,0 63.2% 58.1%
GCL + IPL 0.622 0.499 0.744 74,4 63.2% 58.1%
OPL 0.718 0.607 0.83 36.2 65.8% 60.5%
OS 0.835 0.743 0.926 15.2 84.2% 76.70%
Total reflectance (dB, NRPE)
RNFL 0.646 0.525 0.767 21.8 63.2% 60.5%
GCL + IPL 0.772 0.671 0.873 22.1 73.7% 65.1%
OPL 0.795 0.696 0.894 14.7 71.1% 67.4%
OS 0.892 0.821 0.963 15.9 81.6% 81.4%
concluded that these changes may be related to both the
neuronal and vascular abnormalities [25]. Another report
by Asefzadeh et al. (2008) found that macular and foveal
thickness was significantly thinner with longer duration
of disease in subjects with no or mild DR [26]. Pires et
al. (2002) also suggested that localized areas of retinal
thickening occur in diabetes in the initial stages of reti-
nopathy [27].
Our results suggest that the RNFL, GCL + IPL com-
plex, OPL and OS are more susceptible to initial damage
when comparing MDR with control eyes. Particularly,
the trend observed for the thickness and total reflectance
of the RNFL and GCL + IPL in MDR eyes might be
associated with pathological metabolic changes in the
retina and may reflect neurodegenerative changes in the
diabetic retina. These findings also have possible impli-
cations for the early detection of macular damage in dia-
betes. Because the macular region is rich in retinal gan-
glion cells, it could be suggested that diabetic damage of
this central region might occur early in the disease proc-
ess. In fact, animal models of DR show significant loss
of macular ganglion cells [28-32]. Interestingly, the
thickness and total reflectance of the OPL in MDR eyes
was significantly reduced compared with similar meas-
ures in normal healthy eyes. Previous studies have
shown that not only retinal pericytes and endothelial
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W. Gao et al. / J. Biomedical Science and Engineering 4 (2011) 657-665 663
cells are susceptible to hyperglycemia, but neuroglial
elements of the retina are also involved in the retinal
damage caused by diabetes [28,33,34]. According to
Barber and colleagues, apoptotic cells are likely to in-
clude ganglion cells and other neurons in the retina (such
as cells of the plexiform and nuclear layers) [28]. On the
other hand, the highest AUROC values were obtained
for the OS when comparing MDR with DM eyes. This
particular result might suggest that diabetes also inflicts
additional damage to the outer photoreceptor segment,
which could be an early indication of visual function
degeneration that could be used as an additional indica-
tor to enable the early detection of diabetic retinal dam-
age or disease progression.
In this study, the AUROC results showed a similar
trend for both total reflectance including average values
of reflectivity normalized to the maximum value within
the whole retina (RIR) and reflectivity with normaliza-
tion to the RPE reflectance (NRPE). This similar trend
might rule out the dependence to the sensitivity of the
direction of incidence of the light beam. Taking into ac-
count the RPE layer apparently behaves like a diffuse
reflector, an assumption that could be valid when the
RPE is more or less flat, this layer could be fairly insen-
sitive to the direction of incidence of the light beam.
Accordingly, our results appear not to be affected by the
directionality of the light beam in the OCT system.
There are some potential shortcomings of our study.
Time-domain OCT technology has some limitations
compared to the more advanced OCT technology. In
addition, current OCT devices include different segmen-
tation algorithms and methods for speckle noise removal.
Therefore, data analysis is influenced by special as-
sumptions and technological specifications that are in
place for each individual OCT device. Particularly, ul-
trahigh resolution and spectral-domain technologies fa-
cilitate a more precise delineation of the RPE and inner
segment-outer segment junction of the macular photore-
ceptors [18,21]. Although OCTRIMA is able to extract
the RPE layer in data obtained with time domain OCT,
there is much variability in the segmentation of the RPE
outer boundary due to the lower resolution of deeper
structures extracted by Stratus OCT [17]. Therefore, the
use of advanced OCT devices will remove this short-
coming and improve the reliability of RPE measure-
ments. Future studies will benefit from higher resolution
imaging; an increase in the size of the patient population
studied will also be of importance.
5. CONCLUSIONS
The early results presented have shown this methodol-
ogy could have the potential to differentiate diabetic
eyes with early retinopathy from healthy eyes. Future
studies are needed to determine the accuracy, repeatabil-
ity and full capability of this methodology with more
OCT scans.
6. CONFLICT OF INTEREST
The University of Miami and Dr. Cabrera DeBuc hold a
pending patent used in the study and have the potential
for financial benefit from its future commercialization.
All other authors of the manuscript report no disclosures.
7. ACKNOWLEDGEMENTS
This study was supported in part by a Juvenile Diabetes Research
Foundation grant (JDRF 2007-727), a NIH center grant P30-EY014801,
Department of Defense (DOD-Grant#W81XWH-09-1-0675) and by an
unrestricted grant to the University of Miami from Research to Prevent
Blindness, Inc. The authors would like to express their gratitude to Dr.
Ádám Gy. Tabák (Department of Epidemiology & Public Health, Uni-
versity College London, UK and 1st Department of Internal Medicine,
Budapest, Hungary) for his expert statistical advice.
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