2018 International CET Conference on Control, Communication, and Computing (IC4) | July 05 – 07, 2018 | Trivandrum
Computer Aided Detection of Demarcation line/
Ridges in the Retinal Fundus Images of Preterm
Infants
Gokul G kurup
Kavya J S
Sivakumar R
Department of ECE
College of Engineering Trivandrum
Trivandrum, India
gokulgkurup15@gmail.com
Department of ECE
College of Engineering Trivandrum
Trivandrum, India
kavyajs1992@yahoo.in
Department of ECE
College of Engineering Trivandrum
Trivandrum, India
sivan@cet.ac.in
Abstract—Retinopathy of Prematurity (ROP) is a curable sight
threatening disease which affects the visual system of premature
infants, weighing less than 1750 grams and who are born before
31 weeks of gestation. The characteristic features of the disease
include abnormal blood vessel growth and scar tissue formation
in the retina which may lead to permanent blindness. The risk
of ROP can be reduced if the disease is diagnosed in the early
stage of its development. As per the recommendations of the
International Committee for ROP classification, the detection
of demarcation line/ridge have great prognostic significance as
its presence indicates the beginning stage of the disease. In
the reported work we develop a computer aided diagnostic
system for the early detection of ROP, where the presence of
ridge/demarcation line is identified in the fundus images of the
preterm infants. The developed system uses image enhancement
techniques followed by clustering operation. An image database
with 33 retinal images of preterm infants are graded with the
proposed system and the results obtained matches with that of
the clinical expert annotation with a good accuracy.
Index Terms—Retinopathy of prematurity, demarcation line,
clustering
impairment and childhood blindness. The International Committee for ROP classification [2] categorize the disease into
five stages where the presence of demarcation line is noticed
in first stage followed by ridge formation in the succeeding
stage. Fig.1 shows the retinal images of the five different stages
of ROP. In the initial stage, a faint demarcation line appears
between the vascular and avascular retinal zones and this line
progresses into a structured intra-retinal ridge in the second
stage. The ridge then extends above the plane of retina leading
further to retinal detachment and permanent vision loss.
(c)
(b)
(a)
I. I NTRODUCTION
Computer vision techniques are gaining importance in the
medical research for the fact that there exists the need for
handling a large amount of image data acquired from various
imaging devices. The range of the acquired data include
images obtained from diagnostic machines to microscopic cell
or tissue images. In the developing countries like India the
disease screening programs organised especially in rural areas
create large amount of image data sets for initial screening.
The current screening procedure involves an expert clinician
manually grade these images and make diagnostic decisions.
Literature shows that there exists wide inter-expert variability
in opinion among the experts [1] and the procedure is time
consuming as well as not economical. So there arises the
need for an automated computer aided diagnostic system for
the automated screening of the diseases. In the reported work
retinal image of preterm infants for the automatic detection of
ROP ridges/demarcation line is discussed.
Retinopathy of Prematurity (ROP) is a proliferative retinal
vascular disease which is the leading cause of lifelong vision
978-1-5386-4966-4/18/$31.00 ©2018 IEEE
(d)
(e)
Fig. 1. Stages of Retinopathy of prematurity, (a) Stage I, (b) Stage II, (c)
Stage III, (d) Stage IV and (e) Stage V [5]
ROP has no external signs or symptoms. The only way to
detect its presence is through the analysis of retinal images by
an expert clinician. The literature provides substantial amount
of researches for the automated disease diagnosis [3], [4], [5],
[6]. In all the reported works blood vessel properties like
tortuosity, width, arcade angle etc. are used for the disease
evaluation. But these properties are not always present or
notable in the initial stage images of the disease. So the
detection of demarcation line or ridges plays a major role in
early diagnosis and further treatment of the disease.
A great deal of literature provides information about the
image processing techniques used to detect the ridges. But
in retinal images the contrast values of the ridges and the
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2018 International CET Conference on Control, Communication, and Computing (IC4) | July 05 – 07, 2018 | Trivandrum
Input image
pre-processing
(a)
clustering
contrast
enhancement
(b)
(c)
line/ridge detection
(f)
cluster selection
(e)
(d)
Fig. 2. Schematic of the proposed method which consists of four main steps: (a) Pre-processing, (b) image enhancement, (c) clustering, and (d) line/ridge
detection.
background are almost same which make its detection very
challenging. It is worth to note that related works on ROP
diagnosis based on line/ridge detection is very less. The first
work in this area is the one proposed by Sinthanayothin et
al. [7] where an early stopping watershed algorithm is used
for the detection purpose. In this work preprocessing and
image enhancement plays a major role for emphasizing the
ridges from the background. In the work proposed by Prabakar
et al. [8], a two-dimensional isotropic undecimated wavelet
transform (IUWT) is used for ridge detection. To the best of
our knowledge these are the only two significant works on
ROP detection based on line/ridges.
In this reported work we combined the image pre-processing
results with contrast enhancement and clustering operations.
Once the line/ridges are differentiated we segmented them with
a good accuracy.
II. M ATERIALS AND METHODS
A. Data set used
We use a set of 33 retinal images that we obtained
from Karnataka Internet Assisted Diagnosis of Retinopathy
of Prematurity, (KIDROP) Bangalore, India. The images are
TM
captured with a RetCam3
camera with a field of view of
130 degrees and a resolution of 1600 × 1200 pixels. Of the 33
images, eight contain signs of line/ridge while the remaining
are marked as normal. The images were labelled by a group
of medical experts and a consensus was reached among their
opinions before final labelling.
B. System methodology
The proposed method consists of four main steps namely
pre-processing, image enhancement, segmentation based on
clustering and finally line/ridge detection. Below we elaborate
on each of these steps in detail. Fig.2 shows the schematic of
the proposed work.
1) Pre-processing: The selection of a suitable color space
is very important in this study as line/ridge is not emphasized
equally in different color spaces. Since there is no prior
knowledge about in which color space the line/ridges are
clearly distinguishable, in the preprocessing stage we convert
the input RGB images to different color spaces namely HSV,
YCbCr, HSI, CMY and YIQ. After carefully analysing the
transformed images, it is found that images with demarcation
line/ ridges are much pronounced in the intensity channel I
of HSI (hue, saturation, intensity) colour space. The RGB and
HSI color spaces are mathematically related as:
(
θ
if
B≤G
H=
(1)
360 − θ if
B>G
where
172
(
θ = cos−1
[(R
S =1−
1
2 (R − G) + (R − B)
1
− G)2 + (R − B)(G − B)] 2
3
[min(R, G, B)]
(R + G + B)
I=
1
(R + G + B)
3
)
(2)
(3)
(4)
2018 International CET Conference on Control, Communication, and Computing (IC4) | July 05 – 07, 2018 | Trivandrum
So further analysis are carried out in the intensity channel
derived from HSI color space.
2) Image enhancement: The contrast of preprocessed images are enhanced by different spatial and frequency domain
techniques. While performing the image enhancement the
contrast of retinal blood vessels also gets lifted up along
with the demarcation lines/ridges. Hence the segmentation task
becomes very difficult. We used the combined effect of both
spatial and frequency domain techniques to achieve our desired
objective.
The contrast of the extracted intensity channel image is
enhanced by using morphological top-hat transform [9] which
is a spatial domain technique. The transformation is obtained
by taking the difference between the input image and its
morphological opening by a disc structuring element of radius
12 pixels. Top- hat transformation helps to remove the uneven
background illumination from the image and is mathematically
represented as:
T (I) = I − (I ◦ b)
(5)
where T (I) is the top-hat transformed output of image I, b
is the structuring element and ◦ denotes the morphological
opening operation. Then we apply frequency domain enhancement technique based on Gaussian high pass filtering on the
tophat transformed image. For this the transformed image in
spatial domain is converted into frequency domain and filtered
with a Gaussian high pass filter. The filtered output is transformed back to spatial domain by using inverse discrete 2DFourier transform. Further, the reconstructed image in spatial
domain is enhanced using contrast limited adaptive histogram
enhancement (CLAHE) [10] to obtain a high contrast image
where ridges are highly emphasized.
In order to avoid the false positive detection at the boundary
of the image due to the undesired border effects, masking
is done by using synthetic masks obtained by thresholding
operation.
3) Image clustering: Clustering basically means grouping
similar objects together. k-means clustering [11] is an exploratory data analysis technique which uses a non hierarchical
method of grouping objects together. It classifies n data points
into k clusters by using some minimum distance metric. The
flowchart indicating the k-means clustering operation is shown
in Fig. 3
4) Demarcation line/ridge detection: The clusters obtained
from k-means clustering operation are analysed for the presence of demarcation line/ridges. The great challenge we faced
in this work is the differentiation of ridges from the other
fundus features like the optic disc region, retinal blood vessels
and the boundary pixels. By analysing the clusters we found
that a cluster with the maximum mean has the highest chance
of finding the presence of ridge. Therefore the cluster with
the maximum mean is automatically selected for the further
analysis.
If ridge/line is present we proceed with the further steps
of ridge detection. We extracted the ridges by eroding the
Fig. 3. Flowchart showing the sequence of operations performed during kmeans clustering operation
image by a structuring element and finding the connected
components. Finally the unwanted noises are removed from
the image and color coding is done (red marks the presence
of line/ridge) for better understanding.
III. R ESULTS AND DISCUSSION
Eventhough literature contains a fairly good amount of ROP
related research, works related to the detection of demarcation line/ridge for its diagnosis is very less. The prognostic
significance of demarcation line/ridge indicates the need of
an extensive research in this field for early diagnosis. The
incidence of ROP in India is reported to vary between 3851.9% in low birth weight infants and greater than 80% in
infants born less than 28 weeks of pregnancy [12]. So any
measures which aids the early detection of ROP can reduce
the childhood blindness rate to a great extend.
For the validation of our work we used the images obtained
from KIDROP, Bangalore, India. Since the images are of size
1600 × 1200 pixels, before preprocessing images are resized
using bicubic interpolation where the output pixel value is the
weighted average of pixels in the nearest 4×4 neighbourhood.
The resizing of image reduces the execution time of the
whole process. In the reported work before arriving into the
system methodology like we explained in the previous section,
we used several conventional image processing techniques
to segment out the ridges from the images. But due to the
eqi-contrast ridges and the background, we didnt get any
fruitful results. So we start with verifying ridge contrast in
various color spaces and found that intensity channel of HSI
color space gives optimum results. After selecting this color
channel for further processing contrast enhancement is done
by combining spatial and frequency domain techniques.
Contrast enhanced images are then subjected to clustering
operation by using k-means clustering technique. In this work
the value of k is selected as three, which represents the number
of clusters. In order to differentiate the normal images from
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2018 International CET Conference on Control, Communication, and Computing (IC4) | July 05 – 07, 2018 | Trivandrum
Fig. 4. Image N5 of KIDROP database with line/ridges (a) Input image (b) pre-processed image (c) contrast enhanced image (d)-(f) clustered images using
k means clustering (k=3) (g) detected ridges/line embedded in the original image(a)
Fig. 5. Image N23 of KIDROP database without line/ridges (a) Input image (b) pre-processed image (c) contrast enhanced image (d)-(f) clustered images
using k means clustering (k=3) (g) detected ridges/line embedded in the original image(a)
that with line/ridge contained images, a threshold is set for the
cluster mean. Here we set the value as 0.13 and if the mean of
any cluster is greater than this value, the condition of presence
of ridge is satisfied and if it is less that image is marked as
normal. In our work this threshold selection is the only place
which requires some manual intervention.
To evaluate the performance of the proposed system, we
compute the specificity (Sp) and sensitivity (Se) measures,
which are expressed as:
Sp =
TN
T N +F P
and Se =
TP
T P +F N
were TP, FP, FN and TN represent true positives, false positives, false negatives and true negatives, respectively. Out of
the 33 images (eight with demarcation line/ ridges and the
remaining normal), our system classified 31 images correctly.
All images with ridges are correctly identified and the misclassified two images belong to the healthy class. Hence, TP = 8,
FP = 2, FN = 0 and TN = 23. This means that we achieve a
sensitivity of 1.0 (we detect eight ridge cases) and a specificity
of 0.92. For the true positive case there are some missing
ridge regions seen on the output images, and are under the
acceptable mark. The output obtained for the input image N05
(Fig. 4) and N23 (Fig. 5) of KIDROP data set are shown.
IV. C ONCLUSION
In the proposed work we present a very simple approach
for diagnosing the early stage ROP by detecting line/ridge in
the retinal images of preterm infants. The main hindrance to
the research is the non availability of standard datasets for
testing and validation of developing algorithms. Eventhough
our system claims a high sensitivity, there is a great room
for further improvement. Future works include testing the
algorithm in a large datasets for validation. More advanced
computer vision algorithms can be applied for improving the
results. Since ROP diagnosis by using line/ridge detection is
in its beginning stage, our work is having its own relevance
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2018 International CET Conference on Control, Communication, and Computing (IC4) | July 05 – 07, 2018 | Trivandrum
in the medical field.
ACKNOWLEDGEMENTS
We thank Dr. Anand Vinekar, KIDROP Bangalore for
sharing his valuable thoughts in the clinical side of ROP and
also for providing the real images and its annotations.
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