Comparison of Automatic Blood Vessel
Segmentation Methods in Retinal Images
Maninderjit Kaur1, Er. Navdeep Singh2
1,2Department
of Computer Engineering, Punjabi University Patiala
1maninderdhaliwal61@gmail.com, 2navdeepsony@gmail.com
Abstract: Retinal blood vessel segmentation is an essential step
that can be used in medical treatment for image analysis. Blood
vessel shape indicates different eye diseases like glaucoma,
diabetic retinopathy etc. and the extraction of these vessels can
be done by using different automated segmentation methods. The
comparative analysis of different blood vessel segmentation
methods in retinal images is introduced in this paper. The
algorithms were evaluated on the publicly available DRIVE
database. The database contains retinal images along with the
ground truth images that have been accurately marked by the
experts. Gradient method produces an average sensitivity -83%,
average specificity-94%, and average accuracy -95%, the
morphological operation produces an average sensitivity-83%,
average specificity-94%, and average accuracy-88% and graph
cut method produces an average sensitivity -83%, average
specificity-95%, and average accuracy -96%. Comparative
analysis shows that the graph cut method is more suitable for
blood vessel segmentation as compared to the local entropy
thresholding.
Keywords— retinal blood vessels, segmentation, blood vessel
extraction, morphological operations, first order gradient, graph
cut
I. INTRODUCTION
Retina is a stratum of tissue that senses the light and sends the
images to our brain. The blood vessel carries the blood
throughout the human body. Retinal blood vessels are the
significant indicator for pathologies like diabetes,
hypertension, retinopathy, glaucoma etc. [16]. When the
amount of blood sugar exceeds, shape of vessels changes
which is the result of diabetic retinopathy. DR is an eye
disease which is the main cause of blindness [15]. Due to this,
the vessels may swell up and leak out liquid or even block-off
totally.
Blood vessels are the thin prolonged structure in the retina
with varying length and width. There exist different databases
having different considerations of illumination, resolution,
field of view etc. With proper testing and intervention,
complications of DR can be stopped. The early detection of
DR reduces the danger of glaucoma by 50%.
An automatic segmentation of retinal blood vessels gives an
idea about the positioning of optic nerve, fovea and also gives
necessary knowledge about the aspects of diseases like micro
aneurysms, hemorrhages, exudates etc. Fig. 1 shows retinal
fundus image.
Fig. 1 Retinal Fundus Image [12]
Manual segmentation is a time consuming process and
requires proper training and intelligence but an automated
segmentation reduces the time and provides consistency.
Blood vessel extraction helps us in identification, medical care
and examination of various disorders. The problems are poor
contrast, varying vessels widths, no proper illumination etc.
There exist different techniques for blood vessel
segmentation. The main objective of this paper is to present a
quick method of blood vessel segmentation by comparing
different algorithms.
This paper is categorized as follows: Section II is focused on
the related work about various image segmentation
techniques. Section III is devoted on the proposed
methodology, Section IV presents the experimentation and
results and finally conclusion is given in Section V.
II. RELATED WORKS
In 2007, Ricci and Perfetti [2] give an effectual and
elementary method of blood vessel segmentation using line
operator. When this method is used to deal with the pixels
which are near to a thick blood vessels, this approach results
in false segmentation. To solve this issue, a new retinal blood
vessel segmentation approach based on line operator and edge
detector (RBVSLE) was introduced.
In 2009, Chin-Chen-Chang et al [4] used RBVSLE method to
extract the blood vessels in retinal images. In the first step
RBVSLE enhances the contrast of green component of RGB
retinal image, then edge map is constructed using canny edge
detector and in the final step vessel pixels are segmented using
line operator from vessel growth seed map.
In 2010, Ana G. Salazar-Gonzalez et al [7] proposed an
unsupervised method for blood vessel extraction in retinal
images using graph cut method. In this method enhancement
of the blood vessels is performed followed by rough
segmentation and then graph is constructed to obtain the final
segmentation.
Step I: The preprocessing is done in first step to separate red,
green and blue channels from the input retinal RGB image.
For further processing the output green channel image is used
because green channel consists of maximum information [18],
[19]. Figure [3] shows green channel of an image, figure [4]
shows blue channel of an image and figure [5] shows red
channel of RGB image.
In 2013, M. Maruthusivarani et al [5] presented a technique of
retinal blood vessel segmentation based on morphological
operations using structuring element. In the first step, preprocessing is done and vessels are extracted in the final step.
In 2013, Sanika S Patankar et al [6] described a scheme for
blood vessel segmentation of retinal images using gradient
method. In this method, gradient between vessel pixels and
background pixels is calculated due to intensity variation and
then gradient attributes are used for vessel extraction.
Fig. 3 Green Channel Image
III. PROPOSED METHODOLOGY
Fig. 4 Blue Channel Image
The proposed work is the comparison of the blood vessel
segmentation methods such as vessel extraction based on
morphological operations; graph cut method and gradient
features [5, 6, 7, 9, 11] Diagrammatic representation of our
research methodology is shown in fig [2].
Original Image
Fig. 5 Red Channel Image
Preprocessing
Step II: Computation of Gradient Features [8, 17]
Gradient
Features
Morphological
Graph Cut
Post Processing
Comparative Analysis
Fig. 2 Comparative Analysis
The experimental procedure applied on different techniques is
given below:
3.1 Gradient Method [6]: This method is used for
segmentation of retinal blood vessels based on gradient
features. Gradient between vessel and non-vessel pixels exists
due to variation of intensity between background and retinal
blood vessels. In this algorithm computation of gradient
features of the green channel input image is done followed by
thresholding to convert gray scale image into binary image.
The three steps followed in this algorithm are:
Gradient of retinal image indicates how intensity values are
varying in the particular image. The magnitude of the gradient
gives an idea about the rate of change of intensity variations
and direction of the gradient suggests that in which direction
the image intensities are changing. Thus, to segment blood
vessels from the background gradient features of the retinal
image can be used because there exists intensity difference
between retinal blood vessels and non-vessel pixels i.e.,
background. The gradient feature vector for the given image
function f (x, y) is defined as [8]:
f
 f
 x
 g x

 grad( f )  


 f
g y



 y
Magnitude of the gradient vector is:
mag(f ) 
gx gy
Direction of gradient vector is:
 ( x,
 g y
y )  tan 1 


 gx 







Fig [7] shows the output of gradient method of vessel
extracted images.
Fig. 10 Morphological Image
Fig. 6 Gray Scale Image
Fig. 7 Gradient Scale Image
Step III: Thresholding [10]: An optimal gray-level threshold
value is used to separate objects of interest in an image from
the background. The gray image is changed into binary image.
3.2 Morphological operation [5]:
3.2 Graph Cut Method [7]:
Step I: Separation of three channels: The preprocessing is
done in first step to separate red, green and blue channels from
the input retinal RGB image. For further processing the
output green channel image is used because green channel
consists of maximum information [18] [19].
Morphological operation analyzes an object based on
structuring element. Structuring element can have any shape
and size. The procedure for segmenting the retinal blood
vessels using morphological operations is as follows.
Step I: Separation of three channels:
The preprocessing is done in first step to separate red, green
and blue channels from the input retinal RGB image. For
further processing the output green channel image is used
because green channel consists of maximum information
[18][19].
Fig. 11 Green Channel Image
Fig. 12 Green Channel Image
Step II: A graph G = (V, E) can be partitioned into two
disjoint sets, 𝐴, 𝐵, 𝐴 𝑈 𝐵 = 𝑉, 𝐵 = 0 by simply removing
edges connecting the two parts. The degree of dissimilarity
between these two pieces can be computed as total weight of
the edges that have been removed. In graph theoretic language
it is called the cut:
Fig. 8 Green Channel Image
Fig. 9 Gray Scale Image
Step II: Morphological opening: In the morphological
opening operation erosion followed by dilation is performed.
It smoothens the object, eliminates the small thin protrusions.
Morphological opening is applied using ball shaped
structuring element.
Step III: Thresholding [10]: An optimal gray-level threshold
value is used to separate objects of interest in an image from
the background. The gray image is changed into binary image.
Graph Cut is Delete enough edges so that is each pixel is
(transitively) connected to exactly one label node .Cost of a
cut is the sum of deleted edge weights. Finding min cost cut is
equivalent to finding global minimum of energy function.
Algorithm for extraction of blood vessels using graph cut
method:
1. Sort E (1……m) by non-decreasing edge weight.
2. Start with a segmentation S 0 , where each vertex vi is in its
own component.
3. Repeat step 3 for q = 1, . . . , m.
4. Construct S q given S q−1 as follows. Let vi and vj denotes
vertices connected by the q-th edge in the ordering, i.e., q =
(vi,vj ). If vi and vj are in disjoint components of S q−1 and
w(q) is small compared to the internal difference of both those
components, then merge the two components otherwise do
nothing. More formally, let C q−1 i be the component of S q−1
containing vi and C q−1 j the component containing vj .
5. Return S = S m
Sensitivity measures the proportion of positives which are
correctly identified as such (e.g., the percentage of sick people
who are correctly identified as having the condition), and
is complementary to the false negative rate.
𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
Fig [11] shows the output of graph cut method of vessel
extracted image.
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
Specificity measures the proportion of negatives which are
correctly identified as such (e.g., the percentage of healthy
people who are correctly identified as not having the
condition), and is complementary to the false positive rate.
𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
𝑇𝑁
𝑇𝑁 + 𝐹𝑃
The accuracy is the proportion of true results (both true
positives and true negatives) among the total number of cases
examined.
Fig. 13 Graph cut Image
Step III: Thresholding [10]: An optimal gray-level threshold
value is used to separate objects of interest in an image from
the background. The gray image is changed into binary image.
IV. EXPERIMENTATION AND RESULTS
For analyzing and execution of the above said algorithms the
computational facility with Windows7, Net Beans IDE 8.0,
intelcore2duo, 2.09GHz, 2GB RAM is used. DRIVE database
is used for training and testing the algorithms. It consists of 40
digital images. These images were captured from a Canon
CR5 non-mydriatic 3CCD camera at 45◦ field of view (FOV).
The size of images is 768 × 584 pixels, eight bit per color
channel. The experimental results of our algorithms are
compared with the hand labeled ground truth images
accessible in the DRIVE database. Table [I] shows the four
main possibilities of classification of pixel of retinal image. In
this paper, the retinal vessels are extracted by using graph cut
method, gradient features and morphological operation.
Performance evaluation of our algorithms is done out by
measuring accuracy, sensitivity, specificity, positive
predictive value and negative predictive value.
TABLE I
VESSEL CLASSIFICATION
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃 + 𝑇𝑁
(𝑇𝑁 + 𝐹𝑃 + 𝑇𝑃 + 𝐹𝑁)
The positive predictive value (PPV) is defined as the
probability that subjects with a positive screening test truly
have the disease.
𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑃
The negative predictive value (NPV) is defines as the
probability that subjects with a negative screening test truly
don't have the disease.
𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 =
𝑇𝑁
𝑇𝑁 + 𝐹𝑁
The comparisons of statistical measures are given below in
table II.
TABLE II. STATISTICAL MEASURES
Methods
Factors
GRADIENT
MORPHOLOGICAL
FEATURES
OPERATIONS
GRAPH
CUT
Vessel Present
Vessel Absent
SENSITIVITY
83%
83%
83%
Vessel Detected
True Positive (TP)
False Positive (FP)
SPECIFICITY
94%
94%
95%
Vessel not Detected
False Negative (FN)
True Negative(TN)
ACCURACY
95%
88%
96%
PPV
95%
88%
94%
NPV
95%
88%
93%
Performance metrics of blood vessel segmentation are defined
based on above vessel classification. There are following five
factors based on which performance of algorithms can be
measured.
V. CONCLUSION AND FUTURE SCOPE
[5] M. Maruthusivarani, et.al., “Comparison of Automatic Blood Vessel
Segmentation Methods in Retinal Images,” IEEE, 2013.
Retinal blood vessel segmentation is an important step for
diabetic retinopathy in medical image analysis. In this paper,
blood vessels in retinal images are segmented using gradient
method, morphological operations and graph cut method. The
comparative analysis of above three methods are done and
theses algorithms are evaluated based on performance
measures such as sensitivity, specificity, accuracy, positive
predictive value and negative predictive value. To evaluate the
performance, the three algorithms are applied on publically
available DRIVE database. Gradient method produces an
average sensitivity -83%, average specificity-94%, and
average accuracy -95%, the morphological operation produces
an average sensitivity-83%, average specificity-94%, and
average accuracy-88% and graph cut method produces an
average sensitivity -83%, average specificity-95%, and
average accuracy -96%. The comparison results show that the
performance measures in graph cut method are better than that
in the gradient features and morphological operations to
segment the blood vessels in retinal images when compared
with other techniques. By incorporating some means of better
enhancement techniques and more effective noise reducing
techniques, the accuracy of the algorithm may be improved.
These algorithms may also be applied to publically available
STARE database. As only edges are detected they can be
filled to get the blood vessel. Research should be done in
filling the structures in our final outputs.
[6] Sanika A. Patankar, et.al. , “Gradient Features and Optimal Thresholding
for Retinal Blood Vessel Segmentation,” IEEE Conference on Computational
Intelligence & Computing Research, 2013.
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