a dynamic approach for exudates detection in diabetic ...a dynamic approach for exudates detection...
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A Dynamic Approach for Exudates Detection in
Diabetic Retinopathy Images Using Clustering 1P.V. Rama Raju,
2I.H.S. Mani Sree,
3P. Krishna Kanth Varma and
4G. Nagaraju
1S.R.K.R Engineering College,
Bhimavaram, India.
[email protected] 2S.R.K.R Engineering College,
Bhimavaram, India.
[email protected] 3S.R.K.R Engineering College,
Bhimavaram, India.
[email protected] 4S.R.K.R Engineering College,
Bhimavaram, India.
Abstract Diabetic retinopathy (DR) is a kind of disease that attacks retina of
human eye occurs due to diabetes because of this there is elaboration of
sugar levels in body. Patient loss his vision due to DR; earlier exposure can
diminish the complication of visual detoriation. Existence of micro-
aneurysms, cotton-woolspots, hemorrhages and exudates are indication of
mild DR .Exudates are foremost signs of DR and can be blocked with a
recent diagnosis. Digital fund us image collected from fund us camera
helps in analyzing the exudates in prior way. This paper proposes a
method which has two essential steps they are coarse segmentation
executed by k-means clustering and fine segmentation executed by
morphological image processing for disclosure of exudates on retinal
images with very low contrast. Firstly contrast limited adaptive histogram
equalization technique is used for preprocessing of retinal images. Later
segmentation of the processed images is done through K-Means clustering.
In order to specify these segmented regions into Non-Exudates and
Exudates a special set of features which are based on color and texture are
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derived. Morphological operations are done to get the perfect classification.
As disclosure of exudates existing in limited areas can also be identified
using this technique hence this technique appears encouraging.
Key Words:Diabetic retinopathy, exudates, micro-aneurysms,
hemorrhages, k-means clustering, fund us image, morphological image
processing.
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1. Introduction
Diabetes arises when the body is inadequate to generate or accurately use
insulin. Insulin is the hormone responsible for transforming food into energy for
daily life[1]. In order to reduce the vision loss regular screening is necessary.
Automatic optic disc (OD) detection from retinal fundus images is a very
prominent objective for detection of different types of eye diseases. And using
this step other retinal parts like blood vessels and macula are also identified.
Exudates and hemorrhages are next step identification of abnormalities present
in the retina so that disease severity can be explained for that explanation the
exudates and hemorrhages should be located correctly the OD allows to
construct the coordinate system of retina in effective way hence the correct
position of exudates and hemorrhages are obtained. Optic disc is the major part
and the opening point for the most of the blood vessels that supplies blood to
the retina. In a normal human eye the optic nerve head carries count of 1 to 1.2
million neurons from the eye towards the brain [1]. Due to increase in blood
glucose level their exist some changes in retinal blood vessels which act as a
major cause of Diabetic Retinopathy(DR).Introductory signs of Diabetic
Retinopathy(DR) is identification of exudates. Exudates are yellow-white
lesions with comparatively distinct margins. As the blood vessels get damaged
within the retina which allows the leaking and depositing of the Exudates and
these Exudates are nothing but lipids and proteins. Ophthalmologists detect the
exudates commenced in retina but this exposure is very tough and time taking.
In addition to this, detection can be done manually but it requires chemical
dilation which has very bad impact on patients and again time consuming
process. Henceautomatic screening techniques for exudates is outstanding
process to reduce the time, cost and labor.
Fig. 1(a): Normalretina 1(b): Diabetic Retina
Above figures explain the basic difference between normal and diabetic eye where
fig 1(a) shows the normal retina and fig 1(b) identifies the diabetic retina where
there is clear identification of exudates, hemorrhages, micro-aneurysms. Many
techniques have been proposed to identify the exudates in a given retinal images.
Some of them are discussed below. In [4] proposed a method where
morphological reconstruction techniques are used directly to obtain the exudates
automatically. Another method [5] expresses the detection of exudates, micro-
aneurysms and optic-disc, macula(anatomical structures of retina) are obtained
using a retinal image analysis scheme. But main problem with this method is it
haven’t explained the concept of exact exudates detection hence they considered
some private database of fundus images.[6]This method is very similar to the
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method explained previously [4] where initially optic disc is segmented later
using morphological image processing techniques main feature exudates are
extracted. In. [7,8] detection of exudates are done by three main steps. Initial
step includes the color normalization which is applied to input image later
contrast enhancement is done then using a clustering approach of Fuzzy c-
means clustering segmentation is exhibited. Finally, the third step includes the
detection of exudates using the neural networks[9] .This method proves more
flexible than other techniques for feature extraction of exudates as it considered
the neural networks. Again [10] which classifies the patch in to exudates and
non-exudates using another technique of Support vector machine(SVM).Fuzzy
c-means clustering followed by morphological techniques are used to detect
exudates[11] .
2. Methodology
The basic work span is pre-processing, Optic disc elimination, exudates
extraction and classification.
Fig. 2: Flow-Chart for Detection of Exudates
In the above Fig.2 clearly shows the stages in proposed method.
(i) Image pre-processing
Because of variations in luminosity, contrast and brightness inside retinal
images it wi make it more complicated to extract the retinal features and other
bright features from exudates in images [12]. Finally to lower this issues Image
Pre-Processing is more convenient to make the image suitable for further
process and it will eliminate noise present in the image and the increases the
illumination combination with retinal images . The image preprocessing is
briefly illustrated below.
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HSI Color Space Conversion
Red, green and blue components is a RGB image contains M×N×3 array of
color pixels.RGB image is having initial stage of preprocessing and it is
translated to HSI colorspace. The main reason for adoption of HSI color space
is intensity of matrix can be differentiated from other components for important
data to be needed for exudates diagnosis, it contains intensity matrix the
distance will be calculated between optic disk then pixel value and exudates and
non-exudates pixels can be extracted[12]. Hue is a color attribute that represents
a pure color, Saturation gives a measure of the degree it is the part of white
light mixed with the hue .
Median Filter
The Easiest method to overcome extracted noise without blurring sharp edges
is done by using median filter. It is an effective choice for the removal of
noises especially salt and pepper noise and horizontal scanning images. In
image preprocessing, salt and pepper noise is added to intensity band and
median filter of 3X3 size.
Adaptive Histogram Equalization
The retinal fundus image has illumination in non-uniform way i.e.., they have
variations in brightness[12]. In an image comparison with sides the centre of
the image has more brightness and brightness diminishes as the distance
increases from centre. Hence adaptive histogram equalization(AHE)is analyzed
to provide Uniform illumination for entire image. Using the AHE darkest part
of the input image becomes brighter and bright part which high illumination is
remains constant or reduced to provide even illumination hence the resultant
image is uniform illuminated image[12]. The results are shown below in
fig(3).In this figure (a) shows the original retinal image and (b)shows the
original I band And finally (c) clearly identifies the I band after pre-processing
in which there is abundant increase in the intensity levels.
(a) (b) (c)
Fig. 3: Pre-Processing Result
Optic Disc Detection
The Optic Disc (OD)is the bright appearance on the retina and it is circular.
While processing the OD might misdiagnose as exudates because of its high
contrast similar to the exudates. To diminish this issue firstly theoptic disc
should be removed from the image. OD is identified by its high intensity value
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hence contrast stretching is the best option. It is a linear transform in which
considers Gmin and Gmax where the Gmin assigns the value of 0 to the image
and Gmax assigns the 255 and the remaining gray levels are placed between 0
to 255, enhancing the contrast is mainly originated by covering the entire range
of gray levels. The contrast enhancement in an image is mainly declared using
specific equations taken from paper [13].
Ic1(x,y)=
255
Gmax −Gmin Ip x, y − Gmin (1)
Ip---- preprocessing output image. TheIc1contrast stretched image is shown in
fig 4(a).Now the binarization of image is mainly done using the α1 which is a
simple threshold value where α1=0.9 is considered and the threshold result used
as mask. In order to exclude candidate regions inversion of mask image and
superimposing on the original image is done. As the dilation is important and
required for the morphological reconstruction, and now place the R on the
existing superimposed image is done and given in an equation form as follows,
Ic2=RfI
(Ic1) (2)
The important part of performance is obtained by fitting the contour of marker
image under the image to be masked, for this many dilations of marker image
Ic2under image to be masked fI are continuously imitated[13].Here the
subtraction of original image from the reconstructed image is calculated and the
obtained difference is nothing but the grey level α2thresholding.
Ic3=Tα2
(fI-Ic2) (3)
Automatic detection of threshold is mainly measured using ostu algorithm. By
doing this all the high intensity pixels are retained and rest are removed by fig
4(b).Implementing the closing morphological function ϕ on the resulting image
.A structuring element S1 isused of disc shaped the range is not fixed it can be
varied in this we mainly considered the disc shape of radius six.
Ic4=∅S1(Ic3
) (4)
In general, largest circular area is the brightest part in retinal image and it is the
optic disc. In some situations exudates areas is very huge than the optic disc. In
such cases finding the specific area among all other regions whose shape is
exact circular. Circularity is obtained by
M=4π∗A
p2 (5)
Astatistics of pixels in the elected regions and p sum of pixels perimeter.
Area whose compactness is close to binary one is pure circular one.
Morphological dilation δ on Preferred area where Ic5 ensures the inclusion of all
pixels in OD area.S2 is another structuring element of a flat disc shaped with
radius six.
OD= 𝛿𝑆2 (𝐼𝑐5)(6)
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(a) (b)
(c) (d)
Fig. 4: Optic Disc Detection
(ii) Image Segmentation
Image segmentation is done by considering the k-means clustering concept. The
retinal image has to be classified in terms of clusters to identify the presence of
exudates.
K-Means Clustering
The technique of clustering has been extensively used in computer vision such
as image retrieval and image segmentation . The objective of clustering is to
group together image samples whose features are similar to each other, as well
as separate the dissimilar images. They only have access to the feature vectors
of the images, no information is given on where to place a particular sample
within a partition or what cluster should it be assigned. K-means is one of the
most widely used and simplest of the clustering algorithms and the most suited
method for clustering in pattern recognition. In this paper, we use k-means to
group mammograms into clusters as a preprocessing step of image retrieval. Let
A = (𝑎1,𝑎2….𝑎𝑛 ) be a set of d-dimensional real vectors to be partitioned into a
set of k( ≤ n) clusters, C = (𝑐1, 𝑐2….𝑐𝑘)[14]. Every data point comes under a
single cluster. The task of k-means is to find a partition that minimizes the sum
of distance functions of the points belonging to the cluster from the empirical
mean of that cluster. Let µ𝑖be the empirical mean of the cluster 𝑐𝑖 . The sum of
distance functions of the points of cluster 𝑐𝑖 is given by:
J(𝑐𝑖)= ||𝑎 − 𝜇𝑖||2
𝑎∈𝑐𝑖(7)
is also called the squared-error of the cluster. The objective of k-means is to
minimize the sum of these values for each cluster 𝑐𝑖 belonging to C,
J(k) = ||𝑎 − 𝜇𝑖||2
𝑎∈𝑐𝑖𝑘𝑖=1 (8)
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This is the algorithm for K-means clustering taken from this[14]paper. The
steps involved in this technique are illustrated as follows:
Randomly arrange k points into the space defining data points to be
clustered. These k points become the initial centers of the k clusters.
Assign to each data point the cluster that has the closest centroid.
Recalculate the position of the k centroids. The centroid of the cluster is
counted by taking the empirical mean of all data points related to that
particular cluster.
Repeat steps 2 and 3 until the positions of all the centroids do not
change.
K-means clustering is considered in this rather than other techniques like fuzzy
c-means, modified k-means clustering in all those methods k-means is very
efficient for our application of identifying the exudates as this k-means provides
the interpretation of results in easy way and practical working can be done even
some assumptions will be broken hence it is considered as best for making into
clusters.
(iii) Feature Selection and Extraction
Feature is the most important factor plays a prominent role in the area of image
processing. Before appropriating features, various image preprocessing
techniques like thresholding, resizing, binarization, normalization etc. are tested
on the sampled image. Therefore, extraction of features techniques are practiced
to obtain features which will very much cooperative in recognition and
classification of images. Feature selection plays a key role in many pattern
recognition problems such as image classification Features are nothing but the
diseased areas like exudates and non-exudates if in a retinal image exudates has
to be extracted then exudates becomes the feature and remaining areas are
considered as non- features. More features does not always lead to a better
classification performance, thus feature selection is usually performed to select
a compact and relevant feature subset in order to reduce the dimensionality of
feature space, which will eventually improves the classification accuracy and
reduce time consumption feature selection methods can be classified to two
categories: filter models and wrapper models. Models in filters usually
considers the characteristics of feature data which and are computationally
efficient..Hence now depending upon the color, intensity, texture exudates are
mainly extracted from the retinal images. Therefore exudates are mainly
extracted and differentiated using this features. Mainly two features are selected
as input to k-means clustering is as follows:
1. In the retinal image, exudates are separated from other pixels because of
their high intensity..Filtering operation is mainly applied only on green
part which posses high amount of information rather than red and blue
and reduces the denoising and performs the smoothing[13]. Exudates are
identified by application of median filter on green component of RGB
image as the green component posses more information than the
remaining two. The resultant image is subtracted from the original gray
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scale image. Hence this subtracted image becomes one of the input to
the k-means clustering.
2. Another feature which can be applied as second input to the k- means is
obtained as follows: The retinal images each and every pixel has
different contrast and gray scale values. Exudates contained in the image
are mainly identified by their high grey level and high contrast. For
contrast enhancement CLAHE is applied on green component. There is
a small confusion between exudates and blood vessels as they both are
acquiring the same contrast hence in order to reduce these problem
blood vessels should be eliminated. Hence closing operation ∅is done
for this:
Ig1=∅S3(Ig)(9)
Here Ig is Enhanced green component
For closing operation a structuring element 𝑆3 whose width should be greater
than the blood vessels are considered.In order to differentiate the exudates area
from others. Local variation is selected because the distribution pixel size will
be the direction in separating the exudates from that particular area with the
others since it shows the traits of the closely distributed exudates clusters.
Local Variation is given as
Ig2=
1
N−1 (Ig1
i − μIg1
(x))2i∈Y(x) (10)
N is a number of pixels in Y(x),μIg1
is the mean of Ig1 i and i∈Y(x),x isa set of
pixels in sub-window[13].A window size 9× 9 𝑖𝑠used because good results can
be obtained if this window size is considered there is a chance of losing
exudates if window size is more
Fig. 5: Feature Selection
Hence from the above two features exudates are mainly obtained by
segmentation using k-means clustering algorithm .Scattering of the observations
throughout the image is obtained by a factor called Variance. Small and high
variance explained as: When the observation data is near to the mean and also to
each other the it is small variance in similar to this observation data points are
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distributed far from each other and also to the mean are considered as high
variance. Execution results have shown that the cluster with least variance with
draw the exudates pixels in huge count. Using morphological reconstruction the
one with lowest variance value is opted as an input for fine segmentation of
exudates. The resulting clusters are as illustrated in Fig 6.
(a) (b)
(c)
Fig. 6: K- Means Clustering Output
Morphological Reconstruction
Morphology is a extensive set of image processing operations that process
images based on shapes. Morphological operations consists of structuring
elements and these are applied to an input image, which creates an output
image of the same size the most essential morphological operations are dilation
and erosion.
As the morphological function identifies the useful way of expansion and
compression of the features depending upon the requirement. Hence
consideration of opening and closing is easy way of making the image useful
for the further proceedings. Imv be the minimum cluster variance. A
morphological dilation operator δis applied on selected clusterof disk shape
structuring element S2to involve all the exudates regions present at boarders
they are excluded as they have the low contrast[13].
Idv =δS2 (Imv )(11)
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On the marker image Ig1Morphological reconstruction is implemented. This
reconstruction is the successive geodesic dilation under the mask. In this
reconstruction of non-candidate regions which are non-exudates are done and
removing of exudates candidate regions are done simultaneously. The mask
image is retrieved by placingIdv to zero gray level in the original image. The
process explained above is done to identify the exudates contours and to
segregate them from remaining contrasted regions. The output image Idv 1 is as
shown in Fig 7(a).
Idv 1=
0 𝑖𝑓𝐼𝑑𝑣 ≠ 0
𝐼𝑔1𝑖𝑓𝐼𝑑𝑣 = 0 (12)
Idv 1 under Ig1
using morphological reconstruction is then calculated as follows:
Ig3=RIg1
(Idv 1)(13)
The difference of the original image Ig1and the reconstructed image Ig3
is
applied with a simple thresholding operation produces the output image.
Ig4=Tα2
(Ig1-Ig3
)(14)
To obtain the exudates the optic disk OD should be removed whereas the
above explained process detects and also removes the OD from the above image
which gives the final outcome. The exudates thus obtained are as shown in Fig
7(b).
7(a):Diabetic Retinopathy Image
7(b):Exudates
Fig. 7: Exudates Detection
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3. Results
In table-1 briefly discuss about the retina condition of humans with diabetes and
their stages are shown below. Different retina images are considered in the
below table and depending upon their exudates area diabetic patients disease
severity can be known whether it is mild or severe case.
Below table clearly shows the original image and exudates obtained through it
are compared and depending upon their extreme considerations severity of the
disease is identified.
Table 1: Performance Measure for Exudates Area Calculation and Severity
Identification
4. Conclusion and Future Scope
Initial stage of diabetic retinopathy is identified by exudates. CLAHE is used
for identifying for low contrast images. The second stage image segmentation is
used for enhancement for color image by using K-means clustering that is the
unsupervised clustering algorithm-means clustering is used for more color
information for the result of improvement of classification..Segmentation of
image into exudates and non-Exudates are classified by a special set of
technique called morphological reconstruction. By using this method
extraction of color and texture are obtained by taking some set of features in to
count. Using this way, the exudates are observed and the success rate will be
Retinal
image
Exudates Exud
at-es
area
value
Results Retinal
condition
stages
1310
278
1666
2212
1154
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high. Micro-aneurysm is detected which is one of the earliest symptoms of
Diabetic Retinopathy can be predicted and its performance can be compared in
the future work.
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