Diabetic Retinopathy is a common complication of diabetes that is caused by changes in the blood vessels of the retina. The blood vessels in the retina get altered. Exudates are secreted, micro-aneurysms and hemorrhages occur in the retina. The appearance of these features represents the degree of severity of the disease. In this paper the proposed approach detects the presence of abnormalities in the retina using image processing techniques by applying morphological processing techniques to the fundus images to extract features such as blood vessels, micro aneurysms and exudates. These features are used for the detection of severity of Diabetic Retinopathy. It can quickly process a large number of fundus images obtained from mass screening to help reduce the cost, increase productivity and efficiency for ophthalmologists.

1. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 02 Issue: 02 December 2013 Page No.55-58
ISSN: 2278-2419
55
An Approach for the Detection of Vascular
Abnormalities in Diabetic Retinopathy
Chandrashekar. M. Patil
Associate Professor, Dept of ECE, VVCE, Mysore
Email: patilcm@gmail.com
Abstract-Diabetic Retinopathy is a common complication of
diabetes that is caused by changes in the blood vessels of the
retina. The blood vessels in the retina get altered. Exudates are
secreted, micro-aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. In this paper the proposed approach
detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing
techniques to the fundus images to extract features such as
blood vessels, micro aneurysms and exudates. These features
are used for the detection of severity of Diabetic Retinopathy.
It can quickly process a large number of fundus images
obtained from mass screening to help reduce the cost, increase
productivity and efficiency for ophthalmologists.
Key words - Diabetic retinopathy (DR), blood vessels,
exudates, micro aneurysms, image processing, morphological
processing, optic disc, disease severity.
I. INTRODUCTION
Diabetic Retinopathy is a common complication of diabetes
and the primary cause for visual impairment and blindness in
adults that is caused by changes in the blood vessels of the
retina. The symptoms can blur or distort the patient’s vision.
Retinopathy is often asymptomatic and the patient is unaware
of retinopathy until the eyes are routinely examined or until
visual impairment is detected. It is important to note that it is
not possible to diagnose diabetic retinopathy using laboratory
tests .Regular screening is essential in order detect the early
stages of diabetic retinopathy for timely treatment to prevent
further deterioration of vision. However, a significant shortage
of professional observers has prompted computer assisted
monitoring. The retina is a unique site where the in vivo
microvasculature can be directly visualized and monitored
repeatedly over time. Recent advances in retinal photographic
imaging techniques have facilitated the development of
computer assisted methods to measure and quantify subtle
variations and abnormalities in the retinal microvasculature.
The blood vessels in the retina get altered. Exudates are
secreted, micro aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. Micro aneurysms are focal dilations
of retinal capillaries and appear as small round dark red dots.
Haemorrhages occur when blood leaks from the damaged
retinal vessels. Exudates occur when lipid or fat leaks from
abnormal blood vessel or aneurysms. An early detection and
diagnosis will aid in prompt treatment and a reduction in the
percentage of visual impairment due to these conditions, it will
aid for a better treatment plan and to improve the vision related
quality of life.
II. RETINAL STRUCTURE OVERVIEW
The retina is the delicate, transparent neural tissue that lines the
posterior two-thirds of the eyeball. Macula is the portion of the
retina responsible for central vision. The photoreceptors on the
retina capture light rays and convert them into electrical
impulses which travel along optic nerve to the brain. The nerve
layer of the retina contains blood vessels.
Fig. 1. Features in the retina
Diabetic retinopathy causes damage to the blood vessels in the
retina and as the disease progresses the presence of micro
aneurysms, exudates and new blood vessels can be observed.
Fig. 1 shows the different features present in the normal and
disease retina.
III. I LITERATURE SURVEY
Gerald Liew et al. [1] have highlighted that the quantitative
and qualitative assessments of retinal vasculature demonstrate
a close association of retinal vascular signs to both clinical and
subclinical cerebrovascular and metabolic outcomes.
Identification of retinal blood vessels needs assistance of a
trained grader. Kittipol Wisaeng et al. [2] proposed a method
for detecting the exudates pathologies of diabetic retinopathy
using Fuzzy C-Means (FCM) clustering and morphological
methods. If any applications need to detect maximum number
of exudates pixels or require execution speed, the FCM
clustering technique could be used in isolation. Tomi et al. [4]
demonstrated the protocol with a baseline method including the
available tool kit. But the maturity of the tool must be
estimated before starting the technology transfer from the
research laboratories to practice and industry. Jaspreet et al.
[11] proposed a blood vessel segmentation method using
morphological filters. Increasing the number of filter banks did
not result in significant improvement of result but increased the
time consuming convolution operation. Hussain et al. [12]
proposed a method for the detection of exudates using adaptive
thresholding and classification is proposed in which the retinal
structures are used to remove artifacts from exudate detection
results. This method still needs to be expanded to include all
signs of DR.The motivation behind to put this paper is signs of

2. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 02 Issue: 02 December 2013 Page No.55-58
ISSN: 2278-2419
56
DR disease which are directly related to retinal edema and
visual loss. And also to recommend a technique for automated
diabetic retinopathy mass screening system which offload the
work of ophthalmologists and also decreases the waiting time
of the patients at the hospital to some extent. The techniques
quoted above are not been tested on large volumes of retinal
images. Some of them were found to fail for large numbers of
retinal images, in contrast with the successful performance of
morphological operation. Methods fail if exudates similar in
brightness, color property and size to the optic disk are present.
The micro aneurysms are difficult to segment due to their
similarity in color and proximity with blood vessels. Some
methods have used blood vessel intersection property to obtain
the optic disk. However, they use the whole blood vessel
network which can lead to wrong or inconclusive result
because of noise from the fringe blood vessels.
IV. SYSTEM METHODOLOGY
The automated detection system involves following processes:
(a) Read the fundus image, (b) Pre-Processing, (c) Anatomical
Structure Extraction, (d) Feature Extraction, (e)Disease
SeverityClassification. These techniques are explained in the
following sections.
Fig. 2. Block diagram of overall system
V. IMAGE PRE – PROCESSING
Color fundus image is first converted into a gray-scale/green-
channel image in order to facilitate the blood vessels
segmentation. From visual observation, blood vessels generally
exhibit the greatest contrast from the background in the green
band. Gray-scale image provides only the luminance
information from the color image after eliminating the hue and
saturation. Most of the information needed is contained in the
intensity matrix and hence suitable for diagnosis of Diabetic
Retinopathy.It is necessary to improve the contrast of these
images to provide a better transform representation for
subsequent image analysis steps. CLAHE, contrast limited
adaptive histogram equalization technique is adopted to
perform the contrast enhancement. The contrast especially in
homogeneous areas of intensity can be limited to avoid
amplifying noise. The command ‘imadjust’ adjusts the image
intensity values and increases contrast of the output by
suppressing the darker pixels which tend to contain less
information.The command “bwareaopen” is also used to
remove the small area of pixels considered to be noise after
applying morphological operations. The steps involved in pre-
processing are as shown in fig.3.
(a) (b)
(c) (d)
(e) (f)
Fig. 3. (a) Original fundus, (b) Green channel image, (c) Gray
scale image, (d) Intensity inversed image after histogram
equalization (CLAHE),
(e) Blood vessels with noise, (f) Final blood vessels.
VI. ANATOMICAL STRUCTURE EXTRACTION
The detection of optic disk is a very important task because it
is similar in brightness and contrast to the exudates. The optic
disk is brighter than any other part in the retinal image and is
circular in shape. Hence we create a circular mask for the optic
disk region by finding the brightest pixels in our image that is
being processed.The processing needs to be performed only on
the region of interest (ROI), i.e. the retina. Generally the border
of the image does not contain much of the significant
information.
Thus, the border formation is to clean off the noisy edges and
retain the ROI. There are two methods in detecting the circular
border of the image. The circular border is extracted after
subtracting the dilated image with the eroded image obtained
by applying morphological dilate and erode. Mask for optic
disk and the circular border are as shown in fig.4.
(a) (b)
Fig. 4. (a) Mask for optic disk, (b) Circular border

3. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 02 Issue: 02 December 2013 Page No.55-58
ISSN: 2278-2419
57
VII. FEATURE EXTRACTION
The fundus image is first preprocessed to standardize its size
to576x720. The features such as blood vessels, exudates and
micro aneurysms are detected and extracted using
morphological operations such as dilate, erode, opening and
closing. The “colfilt” filter is used.Following segmentation
AND logic is applied to the binary image to detect the features.
After feature extraction area of each feature is calculated to
classify the severity of the disease. Fig.5. shows features
extracted from a fundus image.
(a) (b)
(c) (d)
Fig. 5. (a) Original fundus, (b) Blood vessels, (c) Exudates,
(d) Micro aneurysms
VIII. DISEASE SEVERITY CLASSIFICATION
In order to detect the severity of Diabetic Retinopathy the area
of the features is calculated. Based on the results of area
computation, the system uses the classification as normal,
mild, severe to identify the stages of non-proliferative diabetic
retinopathy.In the normal stage, there are no micro aneurysms
and exudates. The mild stage is characterized by the presence
of micro aneurysms. In the severe stage, both exudates and
micro aneurysms are present.
IX. RESULTS
TableI shows the extracted feature and disease severity
classification. The input fundus image is processed, after
processing we get segmented images of blood vessels, micro
aneurysms and exudates. In the processing of images area of
all the extracted features are also calculated. Thenby depending
on the area we notify the severity of the disease.
X. CONCLUSION
In summary, a system for the automatic diagnosis of the
primary signs of Diabetic Retinopathy (DR) has been
developed by applying effective image processing techniques
on the fundus images obtained from various databases. By
using MATLAB, the features such as blood vessels, micro
aneurysms and exudates are extracted and detectedby the
applying morphological processing techniques. Based on the
results of area computation of features, the system uses the
classification as normal, mild, severe to identify the stages of
non-proliferative diabetic retinopathy.This system intends to
help ophthalmologists in the screening process to detect
symptoms of Diabetic Retinopathy quickly and more easily.
This can be a preliminary diagnosis tool or decision support
system for ophthalmologists. The proposed technique does not
require any user intervention, and has consistent performance
in both normal and abnormal images. Retinal vascular digital
image analysis will play an ever greater role in clinical
ophthalmology.
ACKNOWLEDGMENT
The authors would like to thank the authors of DRIVE,
STARE, REVIEW and DIARETDB1 databases for making
their data publicly available.
Table I. The Following Table Shows Extracted Features And Disease Severity Classification
REFERENCES
[1] Gerald Liew, Tien y. Wong, Paul Mitchell, Jie Jin Wang. “Retinal
vascular imaging –A New Tool in Micro vascular disease Research”,
Advances in Cardiovascular Imaging, 2008.
[2] KittipolWisaeng, NualsawatHiransakolwong and EkkaratPothiruk.
“Automatic Detection of Exudates in Diabetic Retinopathy Images”,
Journal of Computer Science 8 (8): 1304-1313, 2012 ISSN 1549-3636 ©
2012 Science Publications.
[3] Neelapala Anil Kumar, Mehar Niranjan Pakki. “Analyzing the
Severity of the Diabetic Retinopathy and Its Corresponding
Treatment”, International Journal of Soft Computing and Engineering,
ISSN: 2231-2307, Volume-2, Issue-2, May 2012.
[4] Tomi Kauppi, Valentina Kalesnykiene, Joni-Kristian Kamarainen,
Lasse Lensu, IirisSorri, AstaRaninen, Raija Voutilainen, Hannu Uusitalo,
Heikki Kalviainen and Juhani Pietil . “DIARETDB1 diabetic retinopathy
database and evaluation protocol”.
Sl. No. Input Fundus Image Blood vessels Exudates Micro aneurysms Disease Severity
1.
MILD
2.
SEVERE
3.
NORMAL

4. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume: 02 Issue: 02 December 2013 Page No.55-58
ISSN: 2278-2419
58
[5] Iqbal .M.I, Aibinu .A.M, Gubbal .N.S, Khan.A. “Automatic diagnosis
of Diabetic Retinopathy using Fundus images”, October 2006.
[6] Akara Sopharak, Bunyari Uyyanonvara, Sarah Barman and Tom
Williamson. “Automatic Micro aneurysm Detection from Non-dilated
Diabetic Retinopathy Retinal Images”, Proceedings of the World
Congress on Engineering 2011 Vol II WCE 2011, July 6 - 8, 2011, London,
U.K.
[7] V. Vijayakumari and N. Suriyanarayanan. “Survey on the Detection
Methods of Blood Vessel in Retinal Images”, European Journal of
Scientific Research, ISSN 1450-216X Vol.68 No.1 (2012), pp. 83-92.
[8] M. Ramaswamy, D. Anitha, S. Priya Kuppamal, R. Sudha and S.
Fepslin Athish Mon. “A Study and Comparison of Automated
Techniques for Exudate Detection Using Digital Fundus Images of
Human Eye: A Review for Early Identification of Diabetic
Retinopathy”, ISSN:2229-6093, Tech. Appl., Vol 2(5).
[9] Shilpa Joshi and Dr P.T. Karule. “Retinal Blood Vessel
Segmentation”, International Journal of Engineering and Innovative
Technology (IJEIT), Volume 1, Issue 3, March 2012.
[10]Selvathi D, N.B.Prakash and Neethi Balagopal. “Automated
Detection of Diabetic
Retinopathy for Early Diagnosis using Feature Extraction and Support
Vector Machine”, International Journal of Emerging Technology and
Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 11, November
2012.
[11]Dr. H.P. Sinha and Jaspreet Kaur,“Automated Detection of Vascular
Abnormalities in Diabetic Retinopathy using Morphological
thresholding”, International Journal of Engineering Science & Advanced
Technology,Volume-2, Issue-4, 924 – 931.
[12] Hussain F. Jaafar and Asoke K. Nandi, and Waleed Al-
Nuaimy,“Detection of Exudates from digital fundus images using a
Region-based segmentation Technique”, 19th
European signal processing
Conference, Barcelona, SpainAugust 29 - September 2, 2011.