20120130406019

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN IN –
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH 0976
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME

ENGINEERING AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 6, September – October 2013, pp. 185-194
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2013): 5.8376 (Calculated by GISI)
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IJARET
©IAEME

AN APPROACH FOR DETECTION OF PRIMARY OPEN ANGLE
GLAUCOMA
Nataraj A.Vijapur1, Dr.Rekha Mudhol2, Dr.R.Shrinivasa Rao Kunte3
1

Assistant Professor, Dept. of Electronics & Communication K. L. E. Society’s Dr. M. S. Sheshgiri
College of Engg.&Technology, Belgaum, India
2
Professor & Head, Ophthalmology Department, K. L. E. Society’s Dr.Prabhakar Kore Hospital &
M.R.C., Belgaum, India
3
Professor & Principal, Jawaharlal Nehru National College of Engineering, Shivamogga, India

ABSTRACT
In this paper the research is focused on novel automated classification system for primary
open angle glaucoma, based on image features from fundus photographs. Glaucoma is one of the
most common causes of eye blindness and is becoming even more important considering the ageing
society. A new data-driven approach is developed which requires no manual supervision. Here, our
goal is to establish a screening system that allows fast, robust and automated detection of
glaucomatous changes in the eye fundus. A study done already has revealed that the juxtapapillary
diameters of the retinal vessels such as superior temporal and inferior temporal artery and vein have
been shown to be significantly smaller in glaucomatous eyes than in normal eyes. This aspect is used
by us for Glaucoma suspect Detection. Firstly, disease independent variations, such as nonuniform
illumination, size differences, are eliminated from the images. Region of Interest around Optic disc
which contains the mentioned vessels is extracted via Pre-processing algorithm. Simple vessel
segmentation strategy is used for vessel detection. Vessel Threshold comparator algorithm predicts
the suspect of disease using vessel diameter constraints.
Keywords: ROI, Vessels, Segmentation, Vessel-Diameters, Comparator.
1. INTRODUCTION
Glaucoma belongs to group of eye diseases that can steal sight without warning or symptoms.
The alarming fact about Glaucoma is that it may lead to blindness. Nearly half of those with
Glaucoma do not know they have the disease. This has been shown repeatedly in studies conducted
in developed countries. Glaucoma is a potentially blinding disease that affecting more than 66
million persons worldwide. It is the second leading cause of blindness worldwide. It can be roughly
divided into two main categories, "open-angle" and "closed-angle” Glaucoma. In the healthy eye, a
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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –

clear fluid called aqueous humor circulates inside the front portion of eye. To maintain a constant
healthy eye pressure, eye continually produces a small amount of aqueous humor while an equal
amount of this fluid flows out of your eye. In case of Glaucoma, the aqueous humor does not flow
out of the eye properly. Fluid pressure in the eye builds up and, over time, causes damage to the optic
nerve fibers. Primary Open angle Glaucoma is an eye disorder that characterized by elevated
intraocular pressure (IOP). This increased IOP leads to damage of the optic nerve head. Hence the
disease is characterized by typical changes in the optic nerve with associated visual field defects (the
area seen by the eye). Since the outer portion of the visual field is the first to be affected and most
types of Glaucoma are asymptomatic, the disease is often diagnosed once significant vision/field has
been lost. The most common types of Glaucoma can cause slow and silent loss of vision over years
and hence early detection of the disease is extremely important. A study done already has revealed
that the juxtapapillary diameters of the retinal vessels such as superior temporal and inferior temporal
artery and vein have been shown to be significantly smaller in glaucomatous eyes than in normal
eye[1]. The differences were most marked for the inferior temporal retinal artery, followed by the
superior temporal artery, the inferior temporal vein and finally the superior temporal vein. In the
current work this reduction in vessel diameters is used to detect the presence of disease.
1.1 CURRENT TECHNIQUES TO DETECT PRIMARY OPEN ANGLE GLAUCOMA
Regular Glaucoma check-ups include two routine eye tests: Tonometry and Ophthalmoscopy.
The Tonometry test measures the intraocular pressure (IOP) of the eye. The normal range of this
IOP is in between 10 mmHg and 22 mmHg. Ophthalmoscope is used to examine the inside portion
of the eye, especially the optic nerve. This helps the doctor look at the shape and color of the optic
nerve. If the pressure in the eye is not in the normal range (10mmHg to 22mmHg), or if the optic
nerve looks unusual, then Perimetry test is performed. This helps doctor to detect the blind spots
caused by Glaucoma & to interpret the presence of it. Most often, Perimetry testing is done with a
machine that determines the person's ability to see small dots of light in all areas of the visual field.
Recently automated eye fundus based image processing systems are being developed which predict
the suspect of Glaucoma based on optic cup to disc ratio or optic disc structure detection[2]. These
all methods involve segmentation of optic disc portion by eliminating retinal vessels from eye fundus
image.
In our proposed methodology of Primary Open Glaucoma detection,
we propose an
complimentary method of eliminating optic disc from eye fundus image & vessel segmentation is
used which is the unique feature of the system which helps to detect the disease.
2. IMPLEMENTATION

Fundus
Camera
imaging

o/p

ROI Preprocessing

Vessel
threshold
Comparator

Vessel
segmentation
and diameter
measurement

Fig.1. Block Diagram Of The System
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Block diagram of the proposed system is shown in Fig.1. To start with fundus image of the
eye is obtained using the camera. As mentioned in introduction the juxtapapillary diameters of the
vessels such as superior temporal and inferior temporal retinal artery and vein have been shown to be
significantly smaller in glaucomatous eyes than in normal eyes[1]. Significant diameters which can
be measured are present around the area of optic disc. This area is extracted in ROI (Region of
Interest) Preprocessing. Vessel segmentation consists of obtaining binary map of vessels from the o/p
of preceding stage. Vessel diameter measurement is also done. Vessel threshold comparator predicts
the suspect of Glaucoma depending upon the diameters.

Fig.2. True Color image of Glaucoma

Fig.3. Gray image of Eye fundus Eye fundus

2.1 ROI Pre-processing
This is the unique feature of the system. Firstly, the color image is converted to Gray scale
image as shown in Fig.2 &Fig.3. The median filter is a sliding-window spatial filter which replaces
the center value in the window with the median of all the pixel values in the window. Median
filtering is a nonlinear operation often used to reduce the impulse noise(outlying values, either high
or low). A median filter is more effective than convolution when the goal is to simultaneously reduce
noise and preserve edges. Optic cup in eye fundus image is the brightest area. Significant diameters
which can be measured are present in and around this optic cup and it is the Region of Interest.
Intensity of optic cup is taken as reference and 15% of area including the optic cup is extracted from
image. Extracted image is again converted to RGB format. The algorithm used is shown in Fig.4. and
extracted image is shown in Fig.5.
Gray
image

Median Filter
Equalization

Extract ROI
Based on Intensity
measurement

Fig.4. Algorithm For ROI pre-processing

187

Fig.5. Extracted ROI


2.2 Vessel Segmentation & Diameter measurement
ROI is subjected to suitable mask and Binary image containing only vessels is extracted. The
Isotropic Un-decimated Wavelet Transform (IUWT) is a powerful, redundant wavelet transform
which is used[11]. The effect of applying the IUWT to a fundus image is shown in Fig.6. & Fig.7.
The set of wavelet coefficients generated at each iteration is referred to as a wavelet level, and one
may see that larger features (including vessels) are visible with improved contrast on higher wavelet
levels. Segmentation can then be carried out very simply by adding the wavelet levels exhibiting the
best contrast for vessels and thresholding based upon a percentage of valued coefficients. We set the
threshold to identify the lowest 30% of coefficients as vessels. Objects less than 0.05% of value are
removed.

Fig.6. Image with low wavelet levels

Fig.7. Image with high wavelet levels

The overall effect of applying the IUWT to a fundus image from the database is shown in Fig.8.

Fig.8. Segmented Image

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Vessel diameter Measurement can be put in terms of algorithm as shown in fig.9.
Binary Map of Retinal Vessels

Thining

Centre line computation using
Spline fitting algorithm

Vessel Edge identification

Vessel Diameter computation
using Euclidean distance
Measurement.

Fig.9. Vessel Caliber measurement

2.2.1 Thining
Segmented image is subjected to morphological thinning algorithm. It iteratively removes
exterior pixels from the detected vessels, finally resulting in a new binary image containing
connected lines of ‘on’ pixels running along the vessel centers. The number of ‘on’ neighbors for
each of these pixels is counted. End pixels are identified, and branch pixels are removed. The
removal of branches divides the vascular tree into individual vessel segments in preparation for later
analysis. This is useful because diameters are not well-defined at branches, and also because
diameters measured before a significant branch or bifurcation are not directly comparable with those
measured afterwards, as less blood will flow through the vessel afterwards and there will be a drop in
pressure.
2.2.2 Centre line refinement using spline fitting
The orientation of a vessel segment at any point could be estimated directly from its centre
line, but discrete pixel coordinates are not well suited for the computation of angles. A least-squares
cubic spline (in piecewise polynomial form) is therefore fitted to each centre line to combine some
smoothing with the ability to evaluate accurate derivatives (and hence vessel orientations) at any
location. A parametric spline curve is required, with appropriate parameterization essential to obtain
a smooth centre line. For this we used the centripetal scheme described by Lee [12].
2.2.3 Vessel Edge identification
The next step is to extract vessel border points from the binary retinal maps starting from the
spline-smoothed centreline and the preliminary vessel widths already computed. The goal is to
identify vessel edges using the information given by pixel intensity profile along vessel crosssections. The refined centerline is smooth enough to compute reliably, most of the times, orthogonal
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segments that do not intersect each other. Hence, for each centreline pixel Cj , the perpendicular dj is
computed.

Fig.10. Vessel Edge Identification
2.2.4 Vessel Diameter Measurement
The contours refinement method proposed improves vessel width estimations in binary
images since vascular boundaries are smoothed and the typical indentation of binary edges is
removed. Thus, multiple diameter measurements along the same vessel will not present a high
standard deviation any longer. The vessel width at point Cj lying on the spline-smoothed centre line
is estimated computing the Euclidean distance between points Dj and Ej these are the points
belonging to the two refined contours and lying on segment Dj, orthogonal to centre line at Cj. We
know that according to the Euclidean distance formula, the distance between two points in the plane
with coordinates (x, y) and (a, b) is given by
dist((x, y), (a, b)) = √(x - a)² + (y - b)²

(1)

This distance itself is the diameter of interest.

Fig.11. Euclidean Based Diameter Measurement
2.3 Vessel Threshold Comparator
It was found that the inferior temporal vein decreased from 0.137±0.020mm (mean and
standard deviation) in the normal eyes to 0.125±0.025 mm in the glaucoma group[1]. Presuming a
tube-like form, this represents a decrease of the vessel cross-section area of 16.7% compared with
28.8% for the cross-section reduction of the inferior temporal artery(diameter in normal
eyes:0.109±0.019 mm; glaucomatous eyes: 0.092 ± 0.023 mm). The discrepancy between the
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decrease of artery and vein cross section might be caused by a clinically asymptomatic engorgement
of the venous blood flowing in glaucoma. This observation is put as Threshold to check the threat of
Primary Open Angle Glaucoma. If diameter is within the threshold limits message is displayed as
“Suspect of Primary Open Angle Glaucoma”.
3. RESULTS
Algorithm is implemented using the Matlab (R2010a, The Mathworks), using only the
functions offered by image processing toolbox.

Fig.12. Output indicating Glaucoma suspect
Depending upon the samples collected with Probability of 0.01 the diameter variation for
different ages as given by the system was as given in Figures 13 & 14.

Fig.13. Diameter variation during Training for 50-65 age group
With a probability mentioned previously variation was found to follow the equation
P=98.3744-0.722x

(2)

where x is the age. This was used to decide the threshold for the respective age group
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Fig.14. Diameter variation during Training for 65-80 age group
Here the variation followed the equation
P=74.0925-0.4139x

(3)

Where x is the age.
Fig.15. shows the observations found by our Glaucoma classifier
TABLE. I. Results of Glaucoma Classifier
Age

65 to 80 years

Mean Diameter observed during
training of Classifier for Superior/
Inferior temporal retinal artery
(in terms of Pixel width) for
patients with glaucoma
45±10 units

Threshold set
for Classifier in
terms of Pixel
width

Decision if
Diameter is
below threshold

50±10

Glaucoma suspect

65±10 units

60±10

Glaucoma suspect

50 to 65
years

Fig.15.Table showing results of Experimentation
4. CONCLUSION
Previously Glaucoma detection has been done via optic cup to disc ratio determination and
structure of optic cup[2] and [14]. Ultrasound images of the eye are analyzed to detect the structural
changes in eye[13].Our method can well be applied as an alternative method to aid the Glaucoma
suspect identification.
Vessel caliber measurements not only indicate presence of glaucoma but can be useful for
clinical diagnosis of diabetes Arteriosclerosis and Hypertension. In fact, Diabetic Retinopathy is a
disease that may cause visual impairments in patients suffering from diabetes mellitus which, after
several years, could even lead to blindness. Obtaining a binary vessel map from retinal images
proves to be useful for many different purposes: among others, the evaluation of vessel tortuosity,
often regarded as a symptom of systemic hypertension, and the measurement of vessel caliber as a
bio- marker for cardiovascular diseases. Hence, the developing of a computerized system for retinal
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vessel segmentation would be of great benefit to improve the efficacy of ophthalmologists work,
helping them in the diagnosis of some degenerative pathologies.
ACKNOWLEDGEMENTS
We would like to express our sincere gratitude to Ophthalmology department K. L. E.
Society’s Dr.Prabhakar Kore Hospital & M.R.C., Belgaum, India for providing us with necessary
guidance & resources required for Experimentation. Special thanks to High Resolution Fundus
(HRF) Image Database available on internet, provided by the Pattern Recognition Lab (CS5), the
Department of Ophthalmology, Friedrich-Alexander University Erlangen-Nuremberg (Germany),
and the Brno University of Technology, Faculty of Electrical Engineering and Communication,
Department of Biomedical Engineering, Brno (Czech Republic). We would like to thank Research
Center, E & C department of Jawaharlal Nehru National College of Engineering, Shivamogga, India
for technical guidance & resources without which venture would not have been possible.
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