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ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
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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
Automatic Detection of Microaneurysm in Diabetic
Retinopathy
N.V.Abirami (PG Student)
Dept. of ECE
St.Xaviers Catholic College of Engineering
Chunkankadai
Mrs.C.Helen Sulochana (Professor)
Dept. of ECE
St.Xaviers Catholic College of Engineering
Chunkankadai
Abstract— In this paper an automatic method to
detect microaneurysms (MAs) in retinal eye fundus
image is proposed. MAs are the initial and most frequent
symptom to appear as a result of diabetic retinopathy.
Due to their low size, low contrast and similarity with
blood vessels, automatic detection is still an open
problem. The aim of our paper is to detect fine MAs
even in non-dilated pupils by eliminating the blood
vessels. In main processing section, the blood vessel is
eliminated using Hessian based Frangi vesselness filter.
A morphological operation named extended minima
transform is applied to detect the fine MAs. We have
tested our approach on the publicly available Diaretdb1
database. The detected MAs are validated by comparing
at pixel level with opthalmologist’ hand-drawn groundtruth. The sensitivity, specificity and accuracy are 76.31,
92.92, 80.67 respectively.
Keywords— diabetic retinopathy; microaneurysms; eye
fundus image; retina.
I. INTRODUCTION
Diabetic Retinopathy (DR) is a serious eye disease
in diabetic patients and is the most common cause of
blindness in many countries. Early treatment of DR will
prevent patients to become affected from this condition. To
prevent the risk of blindness, diabetes patients should have
eye screening every year. The screening process is time
consuming and also requires an expert. However, with the
enormous number of patients, the number of
ophthalmologists or experts is not sufficient to cope with all
patients. This is especially in rural areas or if the workload
of local experts is in huge amount. Therefore the computer
aided automated system can help the opthalmologists to
screen and treat the patients more effectively.
Symptoms of DR include dark lesions such as
microaneurysms (MAs) and intraretinal hemorrhages, and
bright lesions, such as exudates and cotton wool spots.
MAs are the focal dilations of retinal capillaries . They
appear as small round dark red dots in the retina which
appeared at the earliest clinically localized characteristic of
DR. MA detection would help to early treatment and thus
prevent the blindness. The MA detection is difficult because
their pixel values are similar to that of retinal blood vessels.
Also, due to its low contrast, MAs are hard to distinguish
from background variations or noise. In this paper we
concentrate on MA detection in non dilated pupils by
eliminating the blood vessels and background variation.
This paper is structured as follows: Section II gives
a summary of previous works on microaneurysm detection.
The block diagram of proposed method is described in
section III. Experimental analysis and further analysis are
105
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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
demonstrated in section IV. Finally conclusions are
presented in section V.
vessel elimination and MA detection in retinal eye fundus
image.
II. RELATED WORK
III. SYSTEM OVERVIEW
Previously published methods for MA detection
works on color images taken on patients with dilated pupils.
In dilated pupils, the MA and other retinal features are
clearly visible. The quality of non-dilated pupil retinal
images will be worse and so other methods fail to detect
MAs with better accuracy .
Neimiejer et al. [1] proposed a red lesion
detection based on a hybrid approach by combining the
prior works of Spencer and Frame. The candidate detection
is based on pixel classification. The detected candidates are
classified using a number of features extracted and kNN
classifier. In [3] the MAs are detected by highlighting the
candidates using contrast normalization technique. The
vessel is removed in order to avoid misclassification of
MAs. The method proposed in [5] is MA detection by
locally matching a lesion template in sub-bands of wavelet
transformed images. Here there is no use for separate
classifier. The method used in [7] segments the retinal blood
vessels using basic line detector and the support vector
machine classifier. In [14] MAs are detected in fluorescein
angiography fundus images using Radon transform and
multi-overlapping windows. Here the blood vessels and
optic nerve head are detected and masked before the MAs
are detected and numbered using radon transform. In [15]
microaneurysm detection is modeled as finding interest
region from an image. A semi-supervised learning approach
is also presented to train the classifier which can detect the
true MAs accurately. MA detection using multi-scale
correlation filtering and dynamic thresholding is presented
in [16]. This method consists of two levels, MA candidate
detection and MA classification. In [19] all possible
candidate regions are extracted. A feature vector depending
upon certain properties such as shape, intensity and statistics
is formed for eac and every candidate. Finally a hybrid
classifier which combines support vector machine, Gaussian
mixture model and multimodel mediod based modeling
approach is employed to improve the accuracy of
classification.
The existing methods consist of many
disadvantages. They fail to detect some microaneurysms.
This is due to their tiny size, low contrast and similarity
with blood vessels. And it is also difficult to detect fine
microaneurysms from non dilated pupils. The main purpose
of our work is automatic MA detection on non dilated
pupils. Our algorithm introduces a combination of blood
The block diagram of the proposed method is
shown in Fig.1. The three main steps in our method is
preprocessing, vessel elimination and MA candidate
detection. Preprocessing includes median filtering, contrast
enhancement and shade correction. We use Hessian based
Frangi Vesselness Filter to extract the blood vessels from
the preprocessed image. Extended Minima Transform is
applied to the preprocessed image. By subtracting the
extracted vessels from transformed image we get the MA
candidates.
A. Preprocessing
The preprocessing is an important step in order to
attenuate noise, non-uniform illumination and low contrast.
Amongst the color image components i.e. red, green and
blue, green-channel provides maximum local contrast
among the image pixel values. As MAs are clearly distinct
from other retinal features in green-channel, the greenchannel IG is first extracted from the RGB image. A median
filtering operation is applied on the green channel to
attenuate the noise. Contrast enhancement is done using
Contrast Limited Adaptive Histogram Equalization
(CLAHE). In order to eliminate non uniform illumination
shade correction is done by combining top hat transform and
bottom hat transform. Normally, the effect of the top-hat
and bottom-hat operations are based on a predefined
neighborhood or structuring element SE, as illustrated in
equations (1) and (2) respectively.
That ( IC ) = IC - ( IC SE)
(1)
Bhat ( IC ) = IC ● SE ) - IC
(2)
The background variation is eliminated using the equation
given below.
Ishade = That – Bhat
(3)
Where Ic – clahe image
Ishade – shade corrected image
Input
Image
Pre
processing
Extended
minina
Transform
106
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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
(9)
C. Candidate Extraction
Fig.1. Block diagram of proposed method
B. Vessel Elimination
Vessels are another important element in the
fundus image that needs to be removed prior to the MA
detection. Because both MA and vessels appear in reddish
color and MAs cannot occur on vessels. Vessels are
eliminate using Hessian based Frangi Vesselness Filter. To
derive geometrical structures which can be regarded as
tubular, the Hessian-based vessel enhancement filters use
eigen values extracted from the Hessian matrix. The Hessian
matrix in the point x at scale σ, Hσ(x), can be effectively
computed using the Gaussian derivative given below.
(4)
where I is the image and Gσ is the gaussian function with
standard deviation σ. The decomposition of second order
structure of the image extracts the eigen values.
In a 2D image, the condition for an ideal tubular structure is,
|λ1|≤|λ2|
(5)
||λ1|| = 0, || λ1 || ≤ || λ2 ||
(6)
The ratio RB = λ1 / λ2 can be used as a vesselness
measure, since it attains its maximum for a blob-like
structure and is near zero whenever the conditions in (5) and
(6) are fulfilled. Therefore a low value in the Hessian norm
ρ = ||Hσ|| =
also indicates a low vesselness. The
vesselness measure for a given scale σ (considering dark
vessels on a bright background) is computed as
(7)
where α,β,c are parameters that control the sensitivity of the
filter to the dissimilarity measures that distinguish between
tube-like and plate-like structures (RA), blob-like (RB) and
background (S)
(8)
Retinal MAs are the focal dilations of retinal
capillaries. Generally, the diameter of a MA lies between 10
and 100µm, but it is always smaller than 125µm. The
extended-minima transform is applied to the shade corrected
image (fshade) image whose gray levels are in the range [0,1].
Extended minima transform represents the regional minima
of h-minima transform. The output image of extended
minima transform fE is a binary image. Here the white
pixels represent the regional minima in the original image.
The threshold value used here is α= 0.05.
fE=extended minima(fshade)
(10)
where fE is the output image and fshade is the shade corrected
image.
The threshold value selection is very important.
Because if α is higher the number of regions will be lower
and if α is lower the number of regions will be higher. A
small change in threshold value can cause the method either
under-segment or over segment the MA. The parameter α is
varied and tested in order to get better sensitivity and
specificity as follows.
α∈{0.01, 0.02, 0.03, 0.04, 0.05, 0.07, 0.09}
(11)
fVEremoved = fE−fvesselT
(12)
In this proposed method, the threshold value is set
using the values that gave highest sensitivity and specificity
in the previous experiment. From the experiments the value
of α = 0.05 give a good balance between the number of
detected MAs and the number of detected spurious objects.
The extracted blood vessels are removed from the resulting
image using the equation,
where fvesslT is the vessel detected image. Subtracting the
vessel from the extended minima transform output will
produce the MA candidates.
IV. EXPERIMENTAL RESULTS
The input eye fundus image is taken from DIARETDB1
database. The database consists of 89 color fundus images,
of which 84 contain atleast mild non-proliferative signs of
diabetic retinopathy and 4 images do not contain any signs of
diabetic retinopathy according to all ophthalmologists
participated in the annotation. The images were taken by the
107
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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
Kuopio University Hospital with single 50 degree field-ofview digital eye fundus camera 1.
Specificity=
(14)
The image from DIARETDB1 database is given as
input. Initially the green channel of the image is extracted.
This green channel image is subjected to median filter and
clahe. Then shade correction is done using top-hat and
bottom-hat transform to avoid non uniform illumination.
From the preprocessed image, the vessel is extracted using
hessian based frangi vesselness filter. After that extended
minima transform is applied on the preprocessed image to
get a thresholded binary image. The extracted vessel is
subtracted from the transformed image to get the MA
candidates.
Accuracy=
(15)
The performance of our method is evaluated by
comparing the MA candidates with ophthalmologists’ handdrawn ground-truth images pixel by pixel. The detected
image and ground truth image are shown in Fig.3.
Sensitivity and specificity are chosen as our performance
measurement of the proposed algorithm at the pixel level.
The performance evaluation using sensitivity and specificity
shows both how accurate our detection is and how
inaccurate our detection can be. Accuracy is the overall perpixel success rate. For this pixel-based evaluation four
values are considered. They are true positive (TP), a number
of MA pixels correctly identified, false positive (FP), a
number of non-MA pixels that are identified wrongly as
MA pixels, false negative (FN), a number of MA pixels that
are not detected and true negative (TN), a number of nonMA pixels which are correctly detected as non-MA pixels.
From these values, the specificity , sensitivity and accuracy
are calculated using the equations given below.
Sensitivity =
Sensitivity, specificity and accuracy for our
proposed method are 83.55, 95.5 and 86.6% respectively. If
the ground-truth image contains no MA then he sensitivity
cannot be calculated in which TP and FN values are all zero.
The sensitivity and specificity are calculated at the optimum
threshold value. Table 1 shows the comparison of various
methods of candidate extractors.
In our method, there are some incorrect MA
detections that are caused by the noise, very small MA, too
blurred MA, faint retinal blood vessels that cannot be
detected or MA appear very faint. The accuracy of MA
detection depends on the success of the blood vessel
detection. For better sensitivity, haemorrhage detection
could also be added to the system.
Table 1 Comparison of various methods of candidate
extractors.
Methods
Cross section profile
analysis
Top hat transform
Circular
hough
transform
Matching
multiple
Gaussian mask
Our method
Sensitivity
72%
Specificity
87%
Accuracy
72.9%
66%
73%
83.1%
67%
82.7%
82.6%
58%
88.7%
80.3%
76.31%
92.92%
80.67%
(13)
(a)
(b)
(c)
(d)
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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
(e)
(f)
(g)
(h)
Fig. 2. MA detected from DIARETDB1 database (a) input image, (b) green channel image, (c) median filtered image,(d) clahe image, (e) shade corrected image,
(f) vessel extracted image, (g) extended minima transform output, (h) MA candidates detected by subtracting the vessel from extended minima transform
(a)
(b)
Fig. 3. Comparision of detected MA with ground truth (a) Detected MA, (b) hand-drawn ground-truth
V. CONCLUSION
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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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