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
Detection of Exudates in Diabetic Retinopathy
A.M.Monisha (PG Student)
Dept. of ECE
St. Xaviers Catholic college of Engineering,
Chukankadai
Mrs.C.Helen Sulochana (Professor)
Dept. of ECE
St.Xaviers Catholic college of Engineering,
Chunkankadai
Abstract— Diabetic Retinopathy (DR) is one of the leading causes of blindness in developed countries. Exudates are one of
the primary signs of this disease. Detection of exudates by ophthalmologists normally requires pupil dilation using a
chemical solution which takes much time and affects patients. This paper investigates and proposes a set of optimally
adjusted morphological operators to be used for exudates detection on diabetic retinopathy patient’s non-dilated pupil
and low-contrast images. The accurate detection of exudates by the elimination of
Optic Disc (OD) is proposed. Because of its bulb shape and its color similarity with exudates, the Optic Disc could be
detected using bit plane slicing operations. The final estimation of exudates can then be obtained by Morphological
operations based on the appearance of exudates. This can obtain a high accuracy in the detection of exudates.
Keywords— Diabetic Retinopathy,OpticDisc,Contrast Limited Adaptive Histogram Equalization component.
I. INTRODUCTION
Diabetes is the commonest cause of blindness in
the working age group in the developed world. Patient’s
sight can be affected by diabetes which causes cataracts,
glaucoma, and most importantly, damage to blood vessels
inside the eye, a condition known as “diabetic
retinopathy”.Diabetic retinopathy is a critical eye disease
which can be regarded as manifestation of diabetes on the
retina. The screening of diabetic patients for the
development of diabetic retinopathy can potentially reduce
the risk of blindness in these patients by 50%.Diabetic
retinopathy is characterized by the development of retinal
microaneurysms, hemorrhages and exudates. One of the
main tasks of an automatic screening system is to detect DR
lesions, such as exudates. Exudates occur when lipid or fat
leaks from abnormal blood vessel or aneurysms. The
amount
of exudates increases as the degree of disease
becomes more severe. So, early detection of exudates is
very important. Exudates are of two types: Hard Exudates
and Soft Exudates. In this hard exudates are lipid formation
leaking from weakened blood vessels with well defined
borders. Soft exudates are pale areas with indistinct margins
in the retina. This paper is structured as follows: Section II
gives a summary of previous works on exudates detection.
The block diagram of the proposed method is presented in
Section III. Experimental results and further analysis are
demonstrated in
Section IV. Finally, conclusions are presented in Section V.
II. RELATED WORK
The accurate detection of the exudates could be
obtained by the elimination of the Optic Disc. The optic
disc is localized by means of its high grey level variation in
[2]. This works well, if there are no or only few pathologies
like exudates that also appear very bright and are also well
contrasted. However no method is suggested for the
detection of the contours.
In [7], an area threshold is used to localize the
optic disc. The contours are detected by means of the
116
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ISSN 2394-3777 (Print)
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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
Hough transform, i.e., the gradient of the image is
calculated, and the best fitting circle is determined. This
approach is quite time consuming and it relies on conditions
about the shape of the optic disc that are not always met.
Sometimes, the optic disc is not even visible entirely in the
image plane, and so the shape is far from being circular or
even elliptic [5]. The approach in [8] is a Hough transforms
based method to detect the contours of the optic disc. Some
problems have been stated if the optic disc does not meet
the shape conditions or if contrast is very low. In [6], the
optic disc localization problem is addressed by back tracing
the vessels to their origin.
The method used in [4] is morphological filtering
techniques and active contours are used to find the
boundary of the optic disc, while, in [1], an area threshold is
used to localize the optic disc and the watershed
transformation to find its contours. However, shape
irregularities or in low contrast could not have been
eliminated. Also in [5], a similar approach is used. But the
algorithm fails when contrast is too low or the red channel
is saturated. Poor quality images affected the separation
result of bright and dark lesions using thresholding and
exudates feature extraction using RRGS algorithm. Zheng
et al [9] detected exudates using thresholding and a region
growing algorithm. Color normalization and local contrast
enhancement followed by fuzzy C-means clustering and
neural networks were used by Osareh et al. [10]. The
system works well only on Luv color space but in the case
of non-uniform illumination the detection accuracy is low.
Mitra et al. [11] applied naïve Bayes classifier for diagnosis
of diseases from retinal image. Most techniques mentioned
earlier worked on dilated pupils in which the exudates and
other retinal features are clearly seen.
Most techniques mentioned for earlier work for
the detection of exudates does not work well on non dilated
pupil images. Based on experimental work reported in
previous work, good quality images with larger fields are
required. The examination time and effect on the patient
could be reduced if the system can succeed on non-dilated
pupils. This paper proposes an exudates detection
techniques based on mathematical morphology on retinal
images of non-dilated pupils that are low quality images.
The proposed method comprises of bit plane
slicing method and it works on low contrast images and it
works if the shape conditions of the image is not met and
also it determines the correct boundary. They works well on
Luv color space and improves the detection accuracy. This
method detects the false positives correctly and poor quality
images does not affect these operations. The sample of
images are choosen from the DIARETDB1 database, a
public database in which the images are acquired using the
standard fundus camera.
III. SYSTEM OVERVIEW
The proposed approach consists of the following
three steps: Elimination of optic disc, Image preprocessing
and Candidate Extraction. The first step Elimination of
optic disc could be done by the bit plane slicing operations
and preprocessing is necessary to enhance the images. In
the last step of Candidate Extraction the candidate regions
are extracted using the Otsu thresholding followed by
Morphological operation . Our proposed method, as
described in detail in the following section, is broadly
shown in Fig.1.1.
A. Elimination of Optic Disc
The optic disc (the bright circular region from
which the blood vessels emanate) is the only area in the
fundus images having the same brightness and color range
like exudates. So, detection of exudates could accurately be
done by extracting the bright yellow regions after
eliminating the optic disc area from the fundus image. The
bit plane decomposition of an 8 bit image yields eight
binary images.
The higher order bit planes contain a majority of
visually significant data while the lower order ones
contribute to more subtle details in an image . On
examining the eight bit planes of the fundus image, the
lower order bit planes are found to carry significant
information regarding the location of the optic disc. The bit
plane slicing operation yields eight bit plane images and
Morphological closing operation is applied on the eighth bit
plane image. The Morphological closing of the image A
with the structuring element SE of radius six is given by,
C=(A•SE)=(A⊕SE)⊖SE
(1)
where ‘⊕’ denotes dilation , ‘⊖’ denotes erosion. Here A
is the input image, SE is the structuring element of size six
is done.
B. Image Preprocessing
The image preprocessing is used to enhance the
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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
25 MARCH 2015
optic disc detected image output. The methods used for
preprocessing are the Median filtering and CLAHE.
Median filtering is a nonlinear method used to remove
noise while preserving edges.
Elimination Of
Image
Candidate
Optic Disc
Preprocessing
Extraction
Fig 1.1 Block Diagram of the Proposed method
The median filter works by moving through the
image pixel by pixel, replacing each value with the median
value of neighbouring pixels. CLAHE stands for Contrast
Limited Adaptive Histogram Equalization. Vessels, MAs,
exudates and noise are dominant after contrast
enhancement. CLAHE is used to prevent the over
amplification of noise.
the following marker
image.
Otsu thresholding is used to automatically perform
clustering-based image thresholding, or, the reduction of a
gray level image to a binary image. Otsu's thresholding
method involves iterating through all the possible threshold
values and calculating a measure of spread for the pixel
levels each side of the threshold, i.e. the pixels that either
fall in foreground or background. The aim is to find the
threshold value where the sum of foreground and
background spreads is at its minimum.The thresholded
image was dilated and it is subtracted with the dilated optic
disc detected image and the morphological operations are
done to obtain the exudates detected output image.
IV. EXPERIMENTAL RESULTS
C. Candidate Extraction
The candidates are the structures that appear in
similar to that of the exudates. The candidates structures
may be cotton wool spots or any other drusens .The
candidate like structures could be detected by the following
steps: Morphological Closing. Local variation on image,
Otsu thresholding, Border removal and Morphological
Reconstruction. Closing is the first step in candidate
extraction. It is an important operator from the field of
mathematical morphology. Closing is a method of dilation
followed by erosion. Closing is the dual of opening, i.e.
closing the foreground pixels with a particular structuring
element, is equivalent to closing the background with the
same element .The Morphological closing of the image A
with the structuring element SE of radius six is given by,
C = (A• SE) = (A⊕SE)⊖SE
(2)
where ‘⊕’ denotes dilation , ‘⊖’ denotes erosion. The
next step is the local variation on image. It can characterize
the texture of an image because they provide information
about the local variability of the intensity values of pixels in
an image. A local variation operator was then applied to the
previous result to get a standard deviation image which
shows the main characterization of the closely distributed
cluster of exudates. The extraction of objects is the next
step after local variation on image. Thus a border-cleaning
procedure was developed based on morphological
reconstruction. The original image is used as the mask and
The proposed method accurately determines the
exudates by the elimination of the Optic Disc. A sample of
input RGB images are converted into its green channel
.Because the green channel exhibits its best contrast on its
background and vessels. A sample of input RGB images are
taken from DIARETDB1 database and the output of the
green channel image is shown in the Fig 1.2 a) and b). After
green channel conversion bit plane slicing is applied and the
highest bit plane image is chosen for further processing. It
contains the major of the information needed for the optic
disc elimination. The bit plane decomposition of the green
channel image yields eight bit planes .The output of the
eight bit plane images are shown in Fig 1.3. The
Morphological closing operation is performed in the eighth
bit plane and the morphological closed image could be
obtained and shown in Fig 1.4.a).The closed image is
complemented and it is multiplied with the green channel
image to obtain the optic disc detected image shown in Fig
1.4 b). The preprocessing methods median filtering and
CLAHE are applied on the optic disc detected image and
the output is shown in Fig 1.4 c) and d).. Applying a closing
operator on the contrast enhanced image will help eliminate
the vessels which may remain in the optic disc region. A
local variation operator was then applied to the previous
result to get a standard deviation image which shows the
main characterization of the closely distributed cluster of
123
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ISSN 2394-3777 (Print)
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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
25 MARCH 2015
exudates and the output is given in Fig 1.4 e) and f).
Fig 1.2 a) Sample of input RGB images
Fig 1.3 Eight bit plane images
Fig 1.2 b)Green channel images
The borders are removed from the resulting image and the
resultant image was threshold at automatically selected grey
levels, using the Otsu algorithm, threshold result were also
included in the candidate region, and was dilated. The result
is shown in the Fig 1.4 g) and h).
The dilated image was subtracted with the dilated
optic disc image and it is multiplied with the dilated
complement image to obtain the Exudates detected output
image and it is shown in Fig 1.4 i).Sample images were
tested using MATLAB. Each image took approximately 3
min to process included the optic disc removal step which
took around 1 m. The previously unclear exudate regions
were visibly highlighted and the exudates can be visibly
observed after the process. The optic disc was also detected
well and removed. Usually there are no exudates pixels
around the optic disc so the removal of the optic disc did
not affect the exudates detection.
The performance of our technique was evaluated
quantitatively by comparing the resulting extractions with
hand-drawn ground-truth images pixel by pixel. Here the
very obvious exudates pixels which are normally bright and
yellowish areas, are marked pixel by pixel. Sensitivity and
specificity were chosen as our measurement of accuracy of
the algorithms at the pixel level. This pixel-based evaluation
considers four values, namely true positive (TP), a number
of exudates pixels correctly detected, false positive (FP), a
number of non-exudates pixels which are detected wrongly
as exudates pixels, false negative (FN), a number of
exudates pixels that were not detected and true negative
(TN), a number of non exudates pixels which were correctly
identified as non-exudates pixels. From these quantities, the
sensitivity and specificity were computed using Eqns (1),(2)
.The accuracy was given in eqn (3).
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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
25 MARCH 2015
compared with the hand-drawn ground-truth images. For
the data set with diabetic retinopathy exudates, the
specificity and sensitivity of the exudates detection are
44.68% and 99.48%,
The retinal images with exudates and normal
retinal images without exudates were processed they were
a)
f)
b)
g)
c)
h)
d)
i)
e)
j)
Fig 1.4 (a)Closed image (b)Optic disc detected image (c) Median filtered image (d)CLAHE image (e)Closed image (f)Local
variation image (g)Thresholded image after border removal (h)Dilated image (i) Exudates detected image
(j)Ground truth image
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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
and accuracy 99.49% respectively. Our algorithm has very high
specificity and accuracy which showed that the algorithm does
not recognize a non-exudates pixel as an exudates pixel. Table I
contains the higher accuracy and sensitivity of the proposed
method in comparison with the other methods.
operations. Thus our proposed method works well on low
contrast images and the accurate detection of fundus images is
done with the elimination of optic disc by bit plane slicing
operations. The performance is also evaluated using ground
truth image and a higher specificity and sensitivity could be
achieved.
Table I Comparison results of the proposed method
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99.32%
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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
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