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Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data |
Biomedical Optics Express, Vol. 4, Issue 1, pp. 134-150 (2013)
http://dx.doi.org/10.1364/BOE.4.000134
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– Abstract
1. Introduction
2. Materials and methods
3. Results and discussion
4. Conclusion
– References and links
– Abstract
1. Introduction
2. Materials and methods
3. Results and discussion
4. Conclusion
– References and links
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Abstract
A fully automated, robust vessel segmentation algorithm has been developed for choroidal OCT, employing multiscale 3D edge filtering and projection of “probability cones” to determine the vessel “core”, even in the tomograms with low signal-to-noise ratio (SNR). Based on the ideal vessel response after registration and multiscale filtering, with computed depth related SNR, the vessel core estimate is dilated to quantify the full vessel diameter. As a consequence, various statistics can be computed using the 3D choroidal vessel information, such as ratios of inner (smaller) to outer (larger) choroidal vessels or the absolute/relative volume of choroid vessels. Choroidal vessel quantification can be displayed in various forms, focused and averaged within a special region of interest, or analyzed as the function of image depth. In this way, the proposed algorithm enables unique visualization of choroidal watershed zones, as well as the vessel size reduction when investigating the choroid from the sclera towards the retinal pigment epithelium (RPE). To the best of our knowledge, this is the first time that an automatic choroidal vessel segmentation algorithm is successfully applied to 1060 nm 3D OCT of healthy and diseased eyes.
© 2012 OSA
1. Introduction
Retinal pathologies are a major health concern in the industrialized world. While previously,
significant research effort has been invested to explore and analyze the outer retinal layers,
little attention has been devoted to non-invasive imaging and analysis of the choroid in-vivo.
The choroid is the layer between the sclera and retinal pigment epithelium (RPE), which contains
vessels and connecting tissue providing oxygen and nourishment to the upper layers. The choroid
is composed of Haller’s layer containing larger vessels, Sattler’s layer
containing medium and small vessels, followed by the choriocapillaris, a fine, thin level of
capillaries. Just underneath the RPE is Bruch’s membrane. Choroidal thickness is related
to age and axial eye length [1,2] but also seems to have a strong relation to pathologies. It can reveal an
increase that correlates with a higher density of vessels in the superficial choroidal layers in
atrophy of the choroidal capillary structure, open-angle glaucoma and neovascularization in the
choriocapillaris [3–6]. It can be assumed that the choroidal vasculature changes as well, though due to the
difficulty of visualizing it in-vivo, the type of change is still largely unknown. A reliable
vessel segmentation algorithm would enable numerous avenues for further clinical research.
M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, K. Kapoor, N. J. Sheen, R. V. North, and W. Drexler, “Three-dimensional 1060-nm OCT: choroidal thickness maps in normal subjects and improved posterior segment visualization in cataract patients,” Invest. Ophthalmol. Vis. Sci. 51(10), 5260–5266 (2010). [CrossRef] [PubMed]
R. Margolis and R. F. Spaide, “A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes,” Am. J. Ophthalmol. 147(5), 811–815 (2009). [CrossRef] [PubMed]
W. R. Green and S. N. Key 3rd, “Senile macular degeneration: a histopathologic study,” Trans. Am. Ophthalmol. Soc. 75, 180–254 (1977). [PubMed]
Z. Q. Yin, T. J. Vaegan, T. J. Millar, P. Beaumont, and S. Sarks, “Widespread choroidal insufficiency in primary open-angle glaucoma,” J. Glaucoma 6(1), 23–32 (1997). [CrossRef] [PubMed]
While angiography has been previously used to visualize the choroidal vasculature, it is an
invasive method that requires injecting an intravenous contrast agent into the patient, making
it potentially dangerous and uncomfortable. Long wavelength optical coherence tomography (OCT)
[7] enables sub-surface, three-dimensional (3D) in-vivo
imaging of the choroid. A high resolution 3D data volume can typically be obtained in a few
seconds.
The low contrast and signal to noise ratio (SNR) of OCT tomograms within the choroid presents
a significant challenge to vessel segmentation algorithms. To the best of the authors’
knowledge, to date no 3D vessel segmentation method has been published that automatically
segments and qualifies choroidal vasculature from OCT tomograms. Methods developed and applied
to vessel segmentation of MRI, CT and other types of data will be discussed. These algorithms
rely on relatively high contrast, while usually trying to achieve very high accuracy.
A straightforward approach is to use some form of edge filtering to find the vessel walls
directly. Gabor filters for vessel wall detection have been used [8], as well as 3D dyadic B-spline wavelets [9].
However, these methods do not perform well if the vessel walls are not well defined. A ridge
based approach uses data intensity represented as ridges, that can be traversed to form a vessel
tree [10]. The drawback of the aforementioned approach is
the need for seed points, requiring manual intervention. Unclear vessel boundaries would present
another problem for these approaches. Region growing methods also rely on seed points [11] and there is a possibility of segmentation leaks. An
alternative is to use moving control points [12],
however, the algorithm can go off track if presented with noisy data. Deformable model based
algorithms, such as snakes, converge slowly and often unreliably and also require some form of
initialization.
R. M. Rangayyan, F. J. Ayres, F. Oloumi, F. Oloumi, and P. Eshghzadeh-Zanjani, “Detection of blood vessels in the retina with multiscale Gabor filters,” J. Electron. Imaging 17(2), 023018 (2008). [CrossRef]
P. J. Yim, P. L. Choyke, and R. M. Summers, “Gray-scale skeletonization of small vessels in magnetic resonance angiography,” IEEE Trans. Med. Imaging 19(6), 568–576 (2000). [CrossRef] [PubMed]
Differential geometry represents data amplitude as an extra dimension, embedding 2D data as a
surface and 3D data as a 4D hypersurface. Local maxima of principal curvatures are likely
candidates for vessel centers, however results were limited to data with high SNR [13].
Artificial intelligence and neural network based methods require difficult to obtain training
sets. Additionally, pixel based classification algorithms [14], would have difficulty segmenting choroidal vessels based on texture features only.
Because OCT background is hard to distinguish from the vessels, Rician distribution background
modeling approach [15] encounters the same problem.
R. Nekovei and Y. Sun, “Back-propagation network and its configuration for blood vessel detection in angiograms,” IEEE Trans. Neural Netw. 6(1), 64–72 (1995). [CrossRef] [PubMed]
Parametric models are the most promising approach, especially if based on multiscale
detection. Vessels can be located by estimating the Hessian matrix, interpreting the data
according to a cylindrical or other model [16].
Eigenvectors obtained using the model give vessel orientation, however the method can be
susceptible to outliers. The data presented had high contrast and even with simple thresholding
produced not as accurate, but reasonable results, which is not true for the OCT choroidal data.
An elliptical fitting model [17] produced good results in
data with relatively highly contrasted vessels compared to the OCT choroidal data. The data
stack size used in the aforementioned paper was far smaller, due to the complex computational
procedure it is questionable how the algorithm would scale up. A thorough review of vessel
segmentation techniques, with more than a hundred algorithms included, covers many more methods
similar to the ones mentioned [18].
J. A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, R. K. Jain, and B. Roysam, “Robust 3-D modeling of vasculature imagery using superellipsoids,” IEEE Trans. Med. Imaging 26(2), 223–237 (2007). [CrossRef] [PubMed]
C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv. 36(2), 81–121 (2004). [CrossRef]
For 3D choroidal vessel quantification, a vessel segmentation algorithm is required that
scales well with highly noisy data. All of the aforementioned methods require relatively high
contrast and rely on various initializations, sequential tracking steps and/or require manual
intervention of the operator which is extremely time costly and limited in the case of 3D data
by human 2D vision (true 3D data can only be perceived as a 2D projection). The proposed method
uses simple multiscale filtering followed by probability estimation of vessel cores to determine
the plausible solutions in a low signal, noisy environment.
2. Materials and methods
High-speed 3D OCT-imaging at 1060 nm was performed with less than 2.5 mW at the cornea, within
safe power limits for a 10-second exposure [19].
Three-dimensional OCT volumes were acquired with 15 to 20 µm transverse resolution,
approximately 7 µm axial resolution in the tissue, and interpolated to 1024 voxels per
depth scan (A-scan). The beam scans across a 36° by 36° field producing 512 by 512
A-scans at 56,000 A-scans/s centered on the fovea (110 B-scans/s, total acquisition time 4.7s
per volume). The spectrometer utilized a line camera with 1024 px (Goodrich SU-LDH2).
B. Povazay, B. Hermann, B. Hofer, V. Kajić, E. Simpson, T. Bridgford, and W. Drexler, “Wide-field optical coherence tomography of the choroid in vivo,” Invest. Ophthalmol. Vis. Sci. 50(4), 1856–1863 (2009). [CrossRef] [PubMed]
The OCT volume was averaged in both transverse directions within a field of approximately
1°, to remove speckle. The “Neat Video” plugin for denoising is applied to
the data, applying adaptive noise reduction based on the background profile to reduce speckle
[20]. For stack registration ImageJ software was used
[21] in case that the input stack was produced by a
standard single raster scan. To improve the signal to noise ratio (SNR), some data sets were
acquired using orthogonal scan pattern. Two 3D volumes of OCT data were acquired with horizontal
and vertical raster scans respectively. The two orthogonal volumes were used to estimate retinal
motion by associating a displacement field with each volume and calculating the displacements,
while optimizing similarity and penalizing motion. This registration method corrects the 3D OCT
data for motion on a per A-scan basis. The two motion corrected 3D OCT data sets are then merged
to create a motion corrected 3D data set with improved SNR [22].
M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3(6), 1182–1199 (2012). [CrossRef] [PubMed]
Choroid was defined as the area between the RPE/Bruch’s membrane/choriocapillaris (RBC)
complex and the choroidal–sclera interface (CSI), which is found using our previously
published algorithm [23]. Although it is not necessary to
find the choroid before performing the vessel segmentation (i.e. treating the whole volume as a
region of interest), it allows for the removal of vessel, drusen and similar structures that
might be found outside of the choroidal area.
V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P. L. Rosin, and W. Drexler, “Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model,” Biomed. Opt. Express 3(1), 86–103 (2012). [CrossRef] [PubMed]
The algorithm was tested on healthy eyes and eyes with various retinal pathologies including
wet and dry age-related macular degeneration (AMD). Testing of the segmentation algorithm was
performed in different clinical cases that are often accompanied by low SNR in deep choroidal
structures, combined with various contrast changes and shadowing artifacts. The results are
meant to show the technical ability of the algorithm, rather than to draw direct, clinical
conclusions.
2.1. Segmentation algorithm overview
The basic idea of the algorithm is to perform multiscale edge filtering and to obtain a
surface normal for each voxel. Irrespective of the strength of an edge, from each voxel a
probability cone can be projected, increasing a counter for each voxel in the path by one,
signifying the likely position of the vessel core. Utilizing this approach, the sensitivity to
poorly defined vessel walls is eliminated. The concept is illustrated in Fig. 1
. The data are downsampled axially to match the correct physical aspect ratio, as it is
important that the vessels are approximately circular. Depending on the cross-section plane
used for visualization, some vessels appear to have a profile significantly different from
circular, but that is irrelevant to the algorithm which operates in 3D.
Fig. 1 Projected probability cones mark the likely position of the vessel core. The actual
algorithm operates fully in 3D, the concept is presented in 2D. Notice the high probability
where the cones intersect.
The algorithm overview is shown in Fig. 2
. For each scale, edge filtering and cone projection procedure is performed.
2.2. Multiscale edge filtering
We apply multiscale filtering using the kernel [−1 −2 0 2 1], a 1D version of a
planar kernel as described in [24]. The kernel satisfies
standard coefficient conditions, where d is a kernel element, and
k is the iterator:
The filtering is performed for all three directions (x, y, z), each filtering operation
giving one vector component, thus producing a normal vector for each voxel. We have used four
scales, spaced so that they do not overlap in the vessel detection (downsampling by a factor of
1, 2, 3, and 4). The last scale could detect the largest choroidal vessels. The cone ray length
was set to four for all scales, effectively giving larger cones in higher scales, enabling
detection of larger vessels.
2.3. Vessel estimation
An ideal probability profile, for the cone projection of an infinite range is
Illustrated in Fig. 3
is how the probability profile A can be obtained using trigonometry.
r is the circle/vessel radius, α is the half cone
width (set to 12.5 degrees). The profile A is actually obtained on a
cylindrical segment, which is how the 2rtan(α) factor
is obtained. We can imagine that for any given x, two components will
contribute; the first for angles from 0 to γ1 and the second
from γ2 to π. That must be multiplied by two due to
the same effect occurring on the bottom side.
Fig. 3 The two contributing factors to the probability profile, that will cover at least the
distance x or longer
This type of profile (Fig. 4
) enables us to effectively threshold the filtered data by using a value set to roughly a
half of the ideal profile plateau (roughly −1.8 to 1.8, Fig. 4) for a certain scale. This will produce a very close estimate of the vessel
core, almost independent of the variation due to noise, because of the steep edge of the
profile. Also, large differences in the height of the profile plateau between the scales, allow
for an effective scale separation, enabling reliable detection of vessels of all sizes.
Fig. 4 An ideal vessel profile, with the vessel core marked in red, and the probability profile
below in blue.
While most of the code was implemented in MATLAB, the cone projection algorithm itself was
written in C and linked with the rest of the code as a mex file. Due to iterating through each
voxel of the stack and projecting a cone for that iteration, such an implementation in MATLAB
would require unreasonable amounts of execution time.
An alternative to using the multiscale approach would be to use a longer range cone in one
scale, and then use an accumulation (counting) array per each voxel of all the contributing
cones. After thresholding, a skeleton (a thin version of the shape equidistant to its
boundaries) could be computed. For each skeleton voxel, the approximate vessel radius could be
then obtained as
num(r) means the number of elements in the accumulation
array with the value r, while the denominator is the normalization term. As a
whole, we want to find the r for which there was the largest contribution of
cones, meaning that that r is probably significant due to a vessel wall.
However, we have found the multiscale method to give better results, and executes faster. The
final addition to the algorithm was an SNR dependent thresholding. In some cases, due to
extremely low signal deep in the choroid (Fig. 5
), we found that the threshold values could be too high. For this reason we obtain a
depth related SNR for all the data, computed on groups of voxels equidistant to the RPE. For
low SNR the thresholding values must be reduced to compensate for poorly defined vessels,
despite the steep edge of the probability profile.
3. Results and discussion
There is a wide variety of data that have to be processed reliably. First, we can demonstrate
that a single standard OCT scan (Fig. 5), as well as a
motion corrected merged OCT volume with increased SNR [22] (Fig. 6
), give similar results, with an 8% classification difference (expressed as the number of
voxels differently classified relative to the total number of voxels). The motion corrected,
merged volume (Fig. 7
) has improved visibility of choroidal features because of increased SNR and speckle
averaging.
M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3(6), 1182–1199 (2012). [CrossRef] [PubMed]
Fig. 6 A cross-sectional 1060 nm OCT tomogram of a healthy eye, using a single scan (top). The
same tomogram is shown with choroidal vessel segmentation (bottom).
Fig. 7 The same tomogram obtained by performing a double orthogonal scan and merging the data
after registration (top), with choroidal vessel segmentation displayed (bottom).
Figure 8
is interesting because it shows that there are also smaller vessels deep in the choroid,
visible due to good SNR, despite the usual trend for larger vessels to dominate in the
Haller’s layer. It is also possible to locate the perforating vessels below the
sclera.
Figure 9
shows a thick healthy choroid. Despite low SNR in the tomogram, all major vessels are
detected by the algorithm.
Figure 10
shows data from a patient with wet AMD, an example of posterior eye pathology. Even with
strong shadowing produced by the RPE detachment, the algorithm still detects vessels in the
choroidal region below the lesion and next to it.
In Fig. 11
it can be seen that very challenging data can be segmented as well. Not only is the
choroid very thin with small vasculature and rapid contrast changes due to RPE atrophy, but the
image is also grainy.
Useful statistics revealing quantitative, morphological detail of the healthy and diseased
choroid can be obtained by using simple measures, such as extracting the number of outer (vessel
walls) and inner vessel pixels. These numbers are related to the distribution of the large and
small choroidal vessels. Figure 12
and Fig. 13
show that the ratio of inner to outer pixels, corresponding to the relative proportion of
larger vessels, decreases from the sclera towards the RPE.
In Fig. 14
the same inner and outer pixel ratio can be calculated by aggregating values in the axial
direction, giving an intuitive 2D projection.
Fig. 14 Healthy eye (subfigures (a), (c), (e)) and a wet AMD (subfigures (b), (d), (f)). (a) and
(b) is the ratio of inner pixels and total choroid thickness, (c) and (d) is the ratio of
inner pixels and total vessel pixels, (e) and (f) is the ratio of outer pixels and total
choroid thickness.
Figure 15
shows 3D rendering of a good contrast, normal choroid. In subfigures with choroid
flattened according to sclera, the depth is color coded, from red to blue.
Figure 16
shows a thick healthy choroid, with an intricate vasculature, especially in
Sattler’s layer. In Fig. 17
a wet AMD is shown, with some disruption in the vasculature of Haller’s layer (not
many vessels in the central region), underneath the RPE detachment. It is difficult to tell
whether this observation corresponds entirely to the biological changes in the tissue, or
whether it might be a result of low SNR in the region below the detachment, due to
hyper-reflectivity of the RPE.
Figure 18
shows a case of dry AMD. The choroidal vasculature is thin and of low density, while
there seems to be an increase in the volume of connective tissue.
4. Conclusion
We have developed and demonstrated an algorithm for automatic vessel segmentation of the
choroid based on vessel “core” multiscale probability filtering. The novel
algorithm performs well on data from healthy subjects and patients with retinal pathologies,
containing artifacts, and extremely low signal due to shadows and depth. The choroidal thickness
correlates negatively with axial length and age. Statistical data such as the ratio between
vessels and non-vessels tissue, the ratio between small and large vessels and visualization of
the large to small vessel distribution can be used to understand choroidal physiological
structure, its changes with age and axial length and differentiate pathological from healthy
variation. The choroid is involved in the pathogenesis of some diseases such as AMD and its
associated buildup of drusen is a risk factor. In addition, despite numerous treatment
modalities for advanced AMD, improvement of vision and a halt to the disease is not always
achieved and continuous retreatment is often necessary. For clinical application, this
information needs to be investigated for its ability to predict predispositions or success of
treatment in diseases that involve the choroid [25] and
have been shown to cause choroidal atrophy [26] or
choroidal neovascularization.
M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, S. Hale, R. V. North, W. Drexler, and N. J. Sheen, “Mapping choroidal and retinal thickness variation in type 2 diabetes using three-dimensional 1060-nm optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 52(8), 5311–5316 (2011). [CrossRef] [PubMed]
R. F. Spaide, “Age-related choroidal atrophy,” Am. J. Ophthalmol. 147(5), 801–810 (2009). [CrossRef] [PubMed]
The run time on an average desktop machine (2 GHz, 8 GB RAM) per stack (512 x 512 x 150) is
about 5 to 10 minutes which can be further reduced by a more efficient implementation, or a GPU
based solution. The proposed method is not only valuable for choroidal vessel segmentation, but
can find use in any type of vessel segmentation when the data signal is weak and the standard
approaches may fail.
Acknowledgments
This research was supported in part by Medical University Vienna,
Macular Vision Research Foundation (MVRF, USA), European Union project FUN OCT (FP7 HEALTH, contract no. 201880), FWF-NFN ‘Photoacoustic imaging in biology
and Medicine’, OENB
Jubiläumsfondsprojekt (14294),
CARL ZEISS Meditec Inc., Femtolasers GmbH and the Christian Doppler
Society (Christian Doppler Laboratory 'Laser development and their
application in medicine'). We acknowledge support from the National Institutes of Health (R01-EY011289-26, R01-EY013178-12, R01-EY013516-09, R01-CA075289-15, R01-NS057476-05, R01-HL095717-04) and the
Air Force Office for Scientific Research (FA9550-10-1-0551 and FA9550-10-1-0063). The
authors also gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical
Technologies (SAOT) by the German Research Foundation
(DFG) in the framework of the German excellence initiative.
References and links
M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, K. Kapoor, N. J. Sheen, R. V. North, and W. Drexler, “Three-dimensional 1060-nm OCT: choroidal thickness maps in normal subjects and improved posterior segment visualization in cataract patients,” Invest. Ophthalmol. Vis. Sci. 51(10), 5260–5266 (2010). [CrossRef] [PubMed] | |
R. Margolis and R. F. Spaide, “A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes,” Am. J. Ophthalmol. 147(5), 811–815 (2009). [CrossRef] [PubMed] | |
W. R. Green and S. N. Key 3rd, “Senile macular degeneration: a histopathologic study,” Trans. Am. Ophthalmol. Soc. 75, 180–254 (1977). [PubMed] | |
D. S. McLeod and G. A. Lutty, “High-resolution histologic analysis of the human choroidal vasculature,” Invest. Ophthalmol. Vis. Sci. 35(11), 3799–3811 (1994). [PubMed] | |
S. H. Sarks, “Ageing and degeneration in the macular region: a clinico-pathological study,” Br. J. Ophthalmol. 60(5), 324–341 (1976). [CrossRef] [PubMed] | |
Z. Q. Yin, T. J. Vaegan, T. J. Millar, P. Beaumont, and S. Sarks, “Widespread choroidal insufficiency in primary open-angle glaucoma,” J. Glaucoma 6(1), 23–32 (1997). [CrossRef] [PubMed] | |
W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008), Vol. 1. | |
R. M. Rangayyan, F. J. Ayres, F. Oloumi, F. Oloumi, and P. Eshghzadeh-Zanjani, “Detection of blood vessels in the retina with multiscale Gabor filters,” J. Electron. Imaging 17(2), 023018 (2008). [CrossRef] | |
W. P. Zhou, W. X. Yu, and H. Z. Shu, “Detection of cerebral vessels in MRA based on 3D steerable filters,” Tien Tzu Hsueh PaoActa Electronica Sinica 34, 1333–1336 (2006). | |
S. Aylward, S. Pizer, D. Eberly, and E. Bullitt, “Intensity ridge and widths for tubular object segmentation and description,” in Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, 1996 (IEEE Computer Society, 1996), pp. 131–138. | |
P. J. Yim, P. L. Choyke, and R. M. Summers, “Gray-scale skeletonization of small vessels in magnetic resonance angiography,” IEEE Trans. Med. Imaging 19(6), 568–576 (2000). [CrossRef] [PubMed] | |
N.-Y. Lee, “Automatic generation of 3D vessels model using vessels image matching based on adaptive control points,” in Sixth International Conference on Advanced Language Processing and Web Information Technology, 2007. ALPIT 2007 (2007). | |
V. Prinet, O. Monaga, C. Ge, S. L. Xie, and S. D. Ma, “Thin network extraction in 3D images: application to medical angiograms,” in Proceedings of the 13th International Conference on Pattern Recognition, 1996 (IEEE Computer Society, 1996), Vol. 3, pp. 386–390. | |
R. Nekovei and Y. Sun, “Back-propagation network and its configuration for blood vessel detection in angiograms,” IEEE Trans. Neural Netw. 6(1), 64–72 (1995). [CrossRef] [PubMed] | |
A. Chung and J. Noble, “Statistical 3D Vessel segmentation using a rician distribution,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI’99, (Springer, 1999), pp. 82–89. | |
K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset, “Model-Based Multiscale Detection of 3D Vessels,” in Workshop on Biomedical Image Analysis (1998), pp. 202–208. | |
J. A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, R. K. Jain, and B. Roysam, “Robust 3-D modeling of vasculature imagery using superellipsoids,” IEEE Trans. Med. Imaging 26(2), 223–237 (2007). [CrossRef] [PubMed] | |
C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv. 36(2), 81–121 (2004). [CrossRef] | |
B. Povazay, B. Hermann, B. Hofer, V. Kajić, E. Simpson, T. Bridgford, and W. Drexler, “Wide-field optical coherence tomography of the choroid in vivo,” Invest. Ophthalmol. Vis. Sci. 50(4), 1856–1863 (2009). [CrossRef] [PubMed] | |
ABSoft, ” Neat Video—best noise reduction for digital video.” | |
P. Thevenaz and M. Unser, “A pyramid approach to sub-pixel image fusion based on mutual information,” in International Conference on Image Processing, 1996. Proceedings (1996), Vol. 1, pp. 265–268. | |
M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3(6), 1182–1199 (2012). [CrossRef] [PubMed] | |
V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P. L. Rosin, and W. Drexler, “Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model,” Biomed. Opt. Express 3(1), 86–103 (2012). [CrossRef] [PubMed] | |
T. A. Chowdhury, O. Ghita, and P. F. Whelan, “Evaluation of 3D gradient filters for estimation of the surface orientation in CTC,” in IMVIP 2006—10th International Machine Vision and Image Processing Conference (2006), paper 4645. | |
M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, S. Hale, R. V. North, W. Drexler, and N. J. Sheen, “Mapping choroidal and retinal thickness variation in type 2 diabetes using three-dimensional 1060-nm optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 52(8), 5311–5316 (2011). [CrossRef] [PubMed] | |
R. F. Spaide, “Age-related choroidal atrophy,” Am. J. Ophthalmol. 147(5), 801–810 (2009). [CrossRef] [PubMed] |
OCIS Codes
(100.0100) Image processing : Image processing
(170.4500) Medical optics and biotechnology : Optical coherence tomography
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(100.3008) Image processing : Image recognition, algorithms and filters
ToC Category:
Image Processing
History
Original Manuscript: October 5, 2012
Revised Manuscript: December 13, 2012
Manuscript Accepted: December 15, 2012
Published: December 17, 2012
Citation
Vedran Kajić, Marieh Esmaeelpour, Carl Glittenberg, Martin F. Kraus, Joachim Honegger, Richu Othara, Susanne Binder, James G. Fujimoto, and Wolfgang Drexler, "Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data," Biomed. Opt. Express 4, 134-150 (2013)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-4-1-134
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References
- M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, K. Kapoor, N. J. Sheen, R. V. North, and W. Drexler, “Three-dimensional 1060-nm OCT: choroidal thickness maps in normal subjects and improved posterior segment visualization in cataract patients,” Invest. Ophthalmol. Vis. Sci.51(10), 5260–5266 (2010). [CrossRef] [PubMed]
- R. Margolis and R. F. Spaide, “A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes,” Am. J. Ophthalmol.147(5), 811–815 (2009). [CrossRef] [PubMed]
- W. R. Green and S. N. Key, “Senile macular degeneration: a histopathologic study,” Trans. Am. Ophthalmol. Soc.75, 180–254 (1977). [PubMed]
- D. S. McLeod and G. A. Lutty, “High-resolution histologic analysis of the human choroidal vasculature,” Invest. Ophthalmol. Vis. Sci.35(11), 3799–3811 (1994). [PubMed]
- S. H. Sarks, “Ageing and degeneration in the macular region: a clinico-pathological study,” Br. J. Ophthalmol.60(5), 324–341 (1976). [CrossRef] [PubMed]
- Z. Q. Yin, T. J. Vaegan, T. J. Millar, P. Beaumont, and S. Sarks, “Widespread choroidal insufficiency in primary open-angle glaucoma,” J. Glaucoma6(1), 23–32 (1997). [CrossRef] [PubMed]
- W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008), Vol. 1.
- R. M. Rangayyan, F. J. Ayres, F. Oloumi, F. Oloumi, and P. Eshghzadeh-Zanjani, “Detection of blood vessels in the retina with multiscale Gabor filters,” J. Electron. Imaging17(2), 023018 (2008). [CrossRef]
- W. P. Zhou, W. X. Yu, and H. Z. Shu, “Detection of cerebral vessels in MRA based on 3D steerable filters,” Tien Tzu Hsueh PaoActa Electronica Sinica34, 1333–1336 (2006).
- S. Aylward, S. Pizer, D. Eberly, and E. Bullitt, “Intensity ridge and widths for tubular object segmentation and description,” in Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, 1996 (IEEE Computer Society, 1996), pp. 131–138.
- P. J. Yim, P. L. Choyke, and R. M. Summers, “Gray-scale skeletonization of small vessels in magnetic resonance angiography,” IEEE Trans. Med. Imaging19(6), 568–576 (2000). [CrossRef] [PubMed]
- N.-Y. Lee, “Automatic generation of 3D vessels model using vessels image matching based on adaptive control points,” in Sixth International Conference on Advanced Language Processing and Web Information Technology, 2007. ALPIT 2007 (2007).
- V. Prinet, O. Monaga, C. Ge, S. L. Xie, and S. D. Ma, “Thin network extraction in 3D images: application to medical angiograms,” in Proceedings of the 13th International Conference on Pattern Recognition, 1996 (IEEE Computer Society, 1996), Vol. 3, pp. 386–390.
- R. Nekovei and Y. Sun, “Back-propagation network and its configuration for blood vessel detection in angiograms,” IEEE Trans. Neural Netw.6(1), 64–72 (1995). [CrossRef] [PubMed]
- A. Chung and J. Noble, “Statistical 3D Vessel segmentation using a rician distribution,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI’99, (Springer, 1999), pp. 82–89.
- K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset, “Model-Based Multiscale Detection of 3D Vessels,” in Workshop on Biomedical Image Analysis (1998), pp. 202–208.
- J. A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, R. K. Jain, and B. Roysam, “Robust 3-D modeling of vasculature imagery using superellipsoids,” IEEE Trans. Med. Imaging26(2), 223–237 (2007). [CrossRef] [PubMed]
- C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36(2), 81–121 (2004). [CrossRef]
- B. Povazay, B. Hermann, B. Hofer, V. Kajić, E. Simpson, T. Bridgford, and W. Drexler, “Wide-field optical coherence tomography of the choroid in vivo,” Invest. Ophthalmol. Vis. Sci.50(4), 1856–1863 (2009). [CrossRef] [PubMed]
- ABSoft, ” Neat Video—best noise reduction for digital video.”
- P. Thevenaz and M. Unser, “A pyramid approach to sub-pixel image fusion based on mutual information,” in International Conference on Image Processing, 1996. Proceedings (1996), Vol. 1, pp. 265–268.
- M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express3(6), 1182–1199 (2012). [CrossRef] [PubMed]
- V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P. L. Rosin, and W. Drexler, “Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model,” Biomed. Opt. Express3(1), 86–103 (2012). [CrossRef] [PubMed]
- T. A. Chowdhury, O. Ghita, and P. F. Whelan, “Evaluation of 3D gradient filters for estimation of the surface orientation in CTC,” in IMVIP 2006—10th International Machine Vision and Image Processing Conference (2006), paper 4645.
- M. Esmaeelpour, B. Povazay, B. Hermann, B. Hofer, V. Kajic, S. Hale, R. V. North, W. Drexler, and N. J. Sheen, “Mapping choroidal and retinal thickness variation in type 2 diabetes using three-dimensional 1060-nm optical coherence tomography,” Invest. Ophthalmol. Vis. Sci.52(8), 5311–5316 (2011). [CrossRef] [PubMed]
- R. F. Spaide, “Age-related choroidal atrophy,” Am. J. Ophthalmol.147(5), 801–810 (2009). [CrossRef] [PubMed]
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