ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
ANTERIOR CHAMBER ANGLE MEASUREMENT USING OPTICAL
COHERENCE TOMOGRAPHY IMAGE
M.Prabha
ME-Applied Electronics
Shri Andal Alagar College of Engineering
Mamandur
Mr.A.Rajan
Assistant Professor (Sr)/ECE
Shri Andal Alagar College of Engineering
Mamandur
Abstract-Optical Coherence Tomography (OCT) is an
imaging modality that has several advantages (e.g. high
resolution and three dimensional imaging) in comparison with
other ophthalmic imaging methods. Angle-closure glaucoma
is a major blinding eye disease and could be detected by
measuring the anterior chamber angle in the human eyes.
High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality
for the anterior segment of the eye.In this paper we present an
approach using morphological operators for the completely
automated segmentation of the anterior chamber region, which
is crucial for the further examination of the geometrical
parameters that indicate the presence of glaucoma.
Keywords: Angle-closure glaucoma, anterior chamber angle,
HD-OCT.
Introduction
GLAUCOMA is one of the major blinding eye diseases
globally. According to, it is the second leading cause of
blindness after cataract and is the leading cause of irreversible
visual loss. Glaucoma is largely caused by poor filtration of
aqueous fluid in the eyeball through the anterior chamber
angle (ACA). If untreated, it leads to higher intraocular
pressure, permanent nerve damage, and blindness. There are
two main types of primary glaucoma, depending on how the
flow of fluid is blocked:
1) Open-angle glaucoma is caused by a gradual hype
functioning of the trabecular meshwork.
2) Angle-closure glaucoma (ACG) is caused by structural
occlusion of the angle by peripheral iris, as shown in Fig.
Figure 1: Causes of Angle-closure glaucoma.
Given that glaucoma is asymptomatic in the early stage and is
often only recognized when the disease is quite advanced and
vision is lost, the detection of ACG using clinical imaging
modalities could aid in arresting its development or slow
down the progression. Anterior chamber angle assessment is
used for the detection of ACG and is essential in deciding
whether or not to perform laser iridotomy. Three approaches,
namely, gonioscopy, ultrasound bio microscopy (UBM), and
anterior segment optical coherence tomography (AS-OCT),
are used for visualizing and measuring the ACA.
A. Gonioscopy
The current reference standard for evaluation of the ACA is
gonioscopy, which was introduced by Trantas in 1899.
Though considered as “gold standard,” gonioscopy is highly
subjective. The definition of angle findings varies across
grading schemes, and there is no universal standard. It is also
prone to potential measurement errors due to how the lens is
placed on the eye and different illumination intensities. As
such, there are severe constraints to its potential as a screening
tool.
B. Ultrasound Biomicroscopy (UBM)
13
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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
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25 MARCH 2015
An alternative approach for viewing ACA is ultrasound
biomicroscopy (UBM), which uses a higher frequency
transducer than regular ultrasound for more detailed
assessment of the anterior ocular structures. Ishikawa et al.
designed a semiautomated program (UBMPro2000) to
calculate several important parameters, based on the manual
identification of the scleral spur, which is prone to
intraobserver and interobserver variability. Although UBM is
useful in quantifying the ACA, the equipment is costly and the
image resolution is sometimes unsatisfactory. Furthermore, it
is neither user nor patient friendlyas a water bath is needed to
image the eye.
C. Anterior Segment Optical Coherence Tomography
(ASOCT)
Anterior segment optical coherence tomography (AS-OCT) is
another instrument for imaging the anterior chamber angle.
Optical coherence tomography is analogous to ultrasound
imaging, as the image is formed by detecting the signal
backscattered from different tissue structures. Instead of sound
waves, light is used for OCT imaging, which avoids the need
for direct contact with the eyes. Furthermore, the use of light
achieves higher spatial resolution than ultrasound. From the
experiments in, AS-OCT is found to be at least as sensitive in
detecting angle closure when compared with gonioscopy. The
existing angle assessment parameters used in AS-OCT are the
same as UBM images. The current Visante built-in angle
assessment software requires substantial user labelling, the
scleral spur, cornea, and iris; hence the measurements are
subjective. The Zhongshan Angle Assessment Program is able
to define the borders of the corneal epithelium, endothelium,
and iris to measure the ACA using the location of scleral spur
as the only observer input. However, it is found that the scleral
spur is not identified in 20% to 30% of Visante OCT images
and measurements using the scleral spur as the landmark are
subjective to intraobserver and interobserver variability.
I. OPTICAL COHERENCE TOMOGRAPHY
Optical coherence tomography (OCT) is a high resolution
cross-sectional imaging modality initially developed for retinal
imaging. Anterior segment OCT (ASOCT) imaging was first
described in 1994 by Izatt et al using the same wavelength of
light as retinal OCT, namely 830nm. This wavelength is
suboptimal for imaging the angle due to limited penetration
through scattering tissue such as the sclera. OCT imaging of
the anterior segment with a longer wavelength of 1310nm was
developed later on and had the advantages of better penetration
through sclera as well as real-time imaging at 8 frames per
second. Currently, there are two commercially produced
dedicated anterior segment devices, the SL-OCT (Heidelberg
Engineering) and the Visante (Carl Zeiss Meditec, Inc.), of
which only the latter is available in the United States. With the
development of Fourier domain OCT (FDOCT) technology,
real-time imaging of the posterior segment has also become
feasible. Several retinal FDOCT devices allow imaging of the
anterior segment, however they still use the shorter wavelength
of 830-870nm with its inherent disadvantages in imaging the
AC angle. The higher resolution provided by FD retinal OCT
devices does have advantages in imaging other structures in the
anterior segment such as the cornea and conjunctiva. Fourier
domain OCT devices operating at the longer wavelength suited
for AC angle imaging have also been described, but these are
not yet commercially available in the United States.
Figure 2: Enhanced Anterior Segment
The Visante OCT has several scanning protocols of which
the Enhanced Anterior Segment Single Scan and the High
Resolution Raw scan are most useful for angle assessment.
Figure is an example of a good-quality Enhanced Anterior
Segment Scan. The important features include good horizontal
and vertical centration of the image within the frame, the
presenceof a reflex saturation beam indicating perpendicularity
of the eye to the scanning beam and minimal tilting of the
image. The scan dimensions are 16 x 6 mm and 4 image frames
are averaged in order to increase the signal to noise ratio. In
this scan protocol, the image is automatically corrected for the
effects of refraction of the scanning beam. The High
Resolution Raw Scan samples 512 A scans/image (as opposed
to 256 A scans/image for the Enhanced scan), there is no image
averaging or refraction correction.
14
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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
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1.1 Qualitative assessment
An important landmark to identify when interpreting ASOCT
images is the scleral spur. This is visible as an inward
projection of the sclera at the junction between the inner scleral
and corneal curvatures. Apposition between the iris and the
inner corneo-scleral wall has been used in several studies as a
qualitative method of detecting angle closure, however it must
be noted that the degree of apposition may be variable and does
not correlate exactly with appositional closure as defined by
gonioscopy. In addition, several studies have shown that the
when using the Anterior Segment Scan protocol which does
not utilize image averaging, the scleral spur is not visible in
about 25% of cases – in this situation, it is still possible to
qualitatively assess irido-corneal apposition in most images.
Load Image
RGB to GRAY
Scale Conversion
Median Filter
1.2 Quantitative assessment
Thersholding
Erosion
Hole Filling
Figure 3: Anterior Segment with angle closure
Quantitative measurement of the AC angle is possible with inbuilt software in most of the anterior segment devices and also
requires identification of the scleral spur. Limitations in
visibility of scleral spur and the wide natural variation in angle
anatomy within the same eye as well as between eyes are
limiting factors in the routine use of quantitative measurement
for angle assessment.
Cropping the
Anterior Chamber
Region
Angle Calculation
II. METHODOLOGY
2.1 Block Diagram
The stages involved in the angle measurement of OCT image
are discussed. It starts with a brief review of the block diagram
processes involved.
Figure 4: Block Diagram Representation
2.2 OCT IMAGE
15
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Vol. II, Special Issue XXIII, March 2015 in association with
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TH
25 MARCH 2015
ASOCT uses the principle of low-coherence interferometry
instead of ultrasound to produce high-resolution, cross
sectional images of the anterior segment of the eye. The
technique measures the delay and intensity of the light
reflected from the tissue structure being analysed and
compares it with the light reflected by a reference mirror. The
combination of these two signals results in interference
phenomenon. The signal intensity depends on the optical
properties of the tissues, and the device uses these signals to
construct a sagittal cross-section image of the structure being
analysed. OCT technology was initially used to produce
images of the posterior segment of the eye by using a
wavelength of 820nm. In 2001, the wavelength was altered to
1310nm to allow better penetration through light retaining
tissues such as the sclera and limbus and to improve
visualization of the anterior segment.Compared with UBM,
this technology provides a higher axial resolution (18um
versus 25um in 50MHz UBM) and faster sampling rate (2.0
kHz versus 0.8 kHz). Another main clinical advantage over
UBM is its ability to provide noncontact scanning in a seated,
upright position. However, the image acquisition can be
affected at times by the superior eyelid, and oblique angles
may allow cross-sectional images. In addition, image
distortions may result from off axis measurements, requiring
special software correction to eliminate the influence of
scanning angle and refractive index of the cornea. Lack of a
coupling medium may affect the image quality due to
abnormalities in the anterior surface of the eye. The major
drawback for AS-OCT is its inability to visualize structures
posterior to the iris due to blockage of wavelength by pigment.
This limits its application in discerning several secondary
causes of angle Closure, such as plateau iris, ciliary body cyst
or tumor, lens subluxation, or ciliary effusions. The two ASOCT devices commercially available are Visante-OCT and
slit-lamp OCT Compared with the Visante-OCT, the SL-OCT
has lower axial and transverse resolution, slower image
acquisition, and requires manual rotation of the scanning
beam.
Figure 5: Anterior segment HD-OCT image. Angle recess—the region
between the cornea and iris. Scleral spur—the point where the curvature of the
angle wall changes, often appearing as an inward protrusion of sclera. Corneal
Endothelium innermost layer of cornea. Corneal epithelium—outer most layer
of cornea.
2.3 RGB TO GRAY SCALE IMAGE
Humans perceive color through wavelength-sensitive sensory
cells called cones. There are three different types of cones,
each with a different sensitivity to electromagnetic radiation
(light) of different wavelength. One type of cone is mainly
sensitive to red light, one to green light, and one to blue light.
By emitting a controlled combination of these three basic
colors (red, green and blue), and hence stimulate the three
types of cones at will, we are able to generate almost any
perceivable color. This is the reasoning behind why color
images are often stored as three separate image matrices; one
storing the amount of red (R) in each pixel, one the amount of
green (G) and one the amount of blue (B). We call such color
images as stored in an RGB format. In gray scale images,
however, we do not differentiate how much we emit of the
different colors. We emit the same amount in each channel.
What we can differentiate is the total amount of emitted light
for each pixel; little light gives dark pixels and much light is
perceived as bright pixels. When converting an RGB image to
gray scale, we have to take the RGB values for each pixel and
make as output a single value reflecting the brightness of that
pixel. One such approach is to take the average of the
contribution from each channel: (R+B+C)/3. However, since
the perceived brightness is often dominated by the green
component, a different, more "human-oriented", method is to
take a weighted average. For Example: 0.3R + 0.59G + 0.11B.
16
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Vol. II, Special Issue XXIII, March 2015 in association with
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A different approach is to let the weights in our averaging be
dependent on the actual image that we want to convert, i.e., be
adaptive. A simple take on this is to form the weights so that
the resulting image has pixels that have the most variance,
since pixel variance is linked to the contrast of the image. In
the applet above, the "optimal projection" calculates how we
should combine the RGB channels in the selected image to
make a gray scale image that has the most variance.
Syntax: I = rgb2gray (RGB)
It converts the true color image RGB to the grayscale
intensity image I. rgb2gray converts RGB images to
grayscale by eliminating the hue and saturation information
while retaining the luminance.
2.4 MORPHOLOGICAL OPERATION
Morphology is a broad set of image processing operations that
process images based on shapes. Morphological operations
apply a structuring element to an input image, creating an
output image of the same size. In a morphological operation,
the value of each pixel in the output image is based on a
comparison of the corresponding pixel in the input image with
its neighbors. By choosing the size and shape of the
neighborhood, you can construct a morphological operation
that is sensitive to specific shapes in the input image.The most
basic morphological operations are dilation and erosion.
Dilation adds pixels to the boundaries of objects in an image,
while erosion removes pixels on object boundaries. The
number of pixels added or removed from the objects in an
image depends on the size and shape of the structuring
element used to process the image. In the morphological
dilation and erosion operations, the state of any given pixel in
the output image is determined by applying a rule to the
corresponding pixel and its neighbors in the input image. The
rule used to process the pixels defines the operation as dilation
or erosion.
2.4.1 Structural Element
An essential part of the dilation and erosion operations is the
structuring element used to probe the input image. A
structuring element is a matrix consisting of only 0's and 1's
that can have any arbitrary shape and size. The pixels with
values of 1 define the neighborhood.
Two-dimensional, or flat, structuring elements are typically
much smaller than the image being processed. The center
pixel of the structuring element, called the origin, identifies
the pixel of interest the pixel being processed. The pixels in
the structuring element containing 1's define the neighborhood
of the structuring element. These pixels are also considered in
dilation or erosion processing. Three-dimensional, or non-flat,
structuring elements use 0's and 1's to define the extent of the
structuring element in the x and y-planes and add height values
to define the third dimension.
2.4.2 Dilation
Dilation adds pixels to the boundaries of objects in an image.
The value of the output pixel is the maximum value of all the
pixels in the input pixel's neighborhood. One immediate
advantage of the morphological approach over low pass
filtering is that the morphological method resulted directly in a
binary image, while low pass filtering started with producing
gray-scale image. The dilation of A by the structuring element
B is defined by:
2.4.3 Erosion
Erosion is the opposite of the dilation. The value of the output
pixel is the minimum value of all the pixels in the input pixel's
neighborhood. In a binary image, if any of the pixels is set to
0, the output pixel is set to 0. The Erosion of A by the
structuring element B is defined by:
2.4.4 Opening
Opening generally smoothes the contour object, breaks narrow
isthmuses, and eliminates thin protrusions. Opening decreases
sizes of the small bright detail, with no appreciable effect on
the darker gray levels, while the closing decreases sizes of the
small dark details, with relatively little effect on bright
features. Opening generally smoothes the contour object,
breaks narrow isthmuses, and eliminates thin protrusions. The
opening of A by B is obtained by the erosion of A by B,
followed by dilation of the resulting image by B.
2.4.5 Closing
Closing also tends to smooth sections of contours but, as
opposed to opening, it generally fuses narrow breaks and long
thin gulfs, eliminates small holes, and fills gaps in the contour.
Closing also tends to smooth sections of contours.
2.5 THRESHOLDING
Thresholding is one of the most important approaches to
image segmentation. From a grayscale image, thresholding
can be used to create binary images. Segmentation is
categorized as
1) Threshold based segmentation,
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2) Edge based segmentation,
3) Region based segmentation,
4) Clustering techniques,
5) Matching.
Threshold based segmentation: Histogram thresholding and
slicing techniques are used to segment the image. They may
be applied directly to an image, but can also be combined with
pre- and post-processing techniques.
Edge based segmentation: With this technique, detected
edges in an image are assumed to represent object boundaries,
and used to identify these objects.
Region based segmentation: Where an edge based technique
may attempt to find the object boundaries and then locate the
object itself by filling them in, a region based technique takes
the opposite approach, by (e.g.) starting in the middle of an
object and then “growing” outward until it meets the object
boundaries.
Clustering techniques:
Although clustering is sometimes used as a synonym for
(agglomerative) segmentation techniques, we use it here to
denote techniques that are primarily used in exploratory data
analysis of high-dimensional measurement patterns. In this
context, clustering methods attempt to group together patterns
that are similar in some sense. This goal is very similar to
what we are attempting to do when we segment an image, and
indeed some clustering techniques can readily be applied for
image segmentation.
Matching:
When we know what an object we wish to identify in an
image (approximately) looks like, we can use this knowledge
to locate the object in an image. This approach to
segmentation is called matching.
Figure 6: Input image
3.1.2 Gray scale converted image
In here we are converting input image to gray scale
image as shown in fig 7.
Figure 7: Gray scale Image
3.1.3 Morphological Operation
III. RESULT ANALYSIS
3.1.1 Input Image
The normal fundus image is taken as input image
with resolution of 565X584 pixels in Tagged Image File
Format (.tif).
Figure 8: Filter Image
18
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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
TH
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Figure 12: Complemented Image
Figure 9: Erod Image
4.1.4 Angle Measurement
Figure 10: Eliminated Image
Figure 13: Anterior Image
Figure 11: Morphological Closed Image
Figure 14: Boundaring Image
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Figure 15: Final output
IV. CONCLUSION
Anterior segment OCT technology enables examiners to
obtain detailed cross-sectional images of the ACA while
avoiding contact with the globe. These images can be analysed
qualitatively. As a result, it is a quick and easily tolerated
procedure for the patient. It also is likely that there is less
distortion of angle morphology due to lack of globe
manipulation. In this paper, we have described an anterior
chamber angle measurement in optical coherence tomography
image. We have used morphological operation that could
assess the anterior chamber angle in HD-OCT images fast
(around 1 s) and accurately. As a future work, better
segmentation methods could be employed to reduce the
speckle noise and eliminate the image dependant threshold
value.
[6] H. Ishikawa, K. Esaki, L. JM, Y. Uji, and R. Ritch,
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Nov./Dec. 1999.
[7] W. P. Nolan, J. L. See, P. T. Chew, D. S. Friedman, S. D.
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“Detection of primary angle closure using anterior segment
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[8] J. W. Console, L. M. Sakata, T. Aung, D. S. F. Man, and
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[9] Visante omni: A new dimension in anterior segment
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[11] H.-T. Wong, M. C. Lim, L. M. Sakata, H. T. Aung, N.
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V. REFERENCES
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