A NEW AND EFFICIENT VESSEL SEGMENTATION METHOD

FROM COLOR RETINAL IMAGES

Alauddin Bhuiyan

1,2

, Ryo Kawasaki

, Ecosse Lamoureux

1,3

, Kotagiri Ramamohanarao

and

Tien Y. Wong

1,3

Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital

The University of Melbourne, Melbourne, Australia

Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia

Singapore Eye Research Institute, National University of Singapore, Kent Ridge, Singapore

Keywords:

Retinal image, Vessel segmentation, Canny edge detection, Gaussian smoothing, Region growing technique,

Edge proﬁling.

Abstract:

Retinal blood vessel changes (e.g., vessel caliber) are important indicators for earlier diagnosis of cardiovas-

cular diseases. To quantify the changes automatically, a reliable vessel detection is essential. However, blood

vessel detection in retinal image is complicated by a huge variation in a number of factors such as local con-

trast, vessel width and vessel central reﬂex. In this paper, we propose a new technique to detect retinal blood

vessels which is able to address these issues. The core of the technique is a new vessel edge selection method

which combines the method of ﬁnding edge pattern and edge proﬁling techniques. Experimental results show

that 92.40% success rate in the identiﬁcation of vessel start-points and 88.73% success rate in tracking the

major vessels.

1 INTRODUCTION

Recent research suggests that retinal vessel caliber

changes can be a predictive factor for cardiovascular

diseases (CVDs) which result in a large number of

deaths in developed and developing countries every

year. According to world health statistics, every year

about 17.1 million people die from CVDs. The num-

ber is expected to increase to 23.4 million by 2030

(World-Health-Statistics, 2008) due to increases of

obesity and aged population. Studies show that reti-

nal arteriolar narrowing is independently associated

with risk of stroke (Wong et al., 2001), heart disease

(Wong et al., 2002a), diabetes (Wong et al., 2002b)

and hypertension (Wong et al., 2004b). Earlier di-

agnosis of these diseases is possible through periodic

screening which can signiﬁcantly reduce the risk of

disease severity and consequently, decrease the num-

ber of deaths and other complications. Blood vessel

detection is the ﬁrst step for determining vessel width

and analyzing the generalized narrowing of retinal

blood vessels.

Although a large number of algorithms (Youssif

et al., 2008),(Al-Diri et al., 2009),(Martinez-Perez et

al., 2007),(Lam and Yan, 2008),(Sofka and Stewart,

2006) have been proposed for the detection of blood

vessels, a huge improvement in detection procedures

remains a necessity for the detection of vessels in the

presence of central reﬂex (Figure 1) and poor contrast

images. Speciﬁcally, these techniques need further

improvement to address vessel detection accurately

with the presence of vessel central reﬂex. The central

reﬂex is a bright strip running down the center which

causes a complicated intensity cross-section. Locally,

this may be hard to distinguish from two side-by-

side vessels and has been discussed previously in

(Sofka and Stewart, 2006). In this paper, we pro-

pose a novel method for vessel segmentation which

addresses these challenges in retinal image modality.

Our paper is a further improvement on the existing

vessel detection algorithm in the presence of vessel

central reﬂex.

(a) (b)

Figure 1: A retinal image (a) and cropped image showing

the retinal artery, vein and the central reﬂex (b).

467

Bhuiyan A., Kawasaki R., Lamoureux E., Ramamohanarao K. and Y. Wong T..

A NEW AND EFFICIENT VESSEL SEGMENTATION METHOD FROM COLOR RETINAL IMAGES.

DOI: 10.5220/0003161404670471

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 467-471

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The rest of the paper is organized as follows. Sec-

tion 2 describes our proposed vessel segmentation

method which includes the edge detection, edge re-

construction, edge proﬁling and ﬁltering, and ﬁnally,

vessel identiﬁcation. Section 3 provides the Experi-

mental Results and Discussion. Conclusion and fu-

ture research directions are drawn in section 4.

2 THE PROPOSED METHOD

We have proposed a method for vessel detection

through edge tracking which is reported in (Bhuiyan

et al., 2010). In this paper, we further improved the

method by applying canny edge detection, edge re-

construction and edge proﬁling method. In our pro-

posed method, blood vessels are extracted based on

edge tracking and vessel morphological information.

The overall method for vessel detection is shown in

Figure 2.

Figure 2: The overall method of retinal blood vessel detec-

tion.

2.1 Edge Detection

As our ultimate focus is on the measurement of vessel

caliber which requires vessel edge information, there-

fore, we apply edge based vessel segmentation ap-

proach. To detect vessel edges, we apply Canny edge

detection algorithm (Canny, 1986). We mentioned

that retinal image contrast varies hugely in image lo-

cal position. For this type of scenario, we consider

Canny edge detection algorithm to mark as many real

edges in the image as possible. We consider retinal

Green channel image which has the highest contrast

between vessel and background for all three color

channels in an RGB image. To remove impulse noise

and other abrupt artifacts from the Green channel im-

age, we apply Median ﬁltering and then apply canny

edge detection algorithm. This approach allows us to

achieve better results for edge detection.

We note that our main focus is to detect the ves-

sel from zone B area, from which the vessel caliber

is computed and the original Central Retinal Artery

Equivalent (CRAE) and Central Retinal Vein Equiv-

alent (CRVE) formulae are derived (Hubbard et al.,

1999). Therefore, we aim to trace the vessels from the

zone B area only. Zone B is the circular region which

starts at 1*OD-diameterand ends at 1.5*OD-diameter

from the OD-center in the retinal image and signif-

icantly used for retinal blood vessel analysis (Wong

et al., 2004). The zone B area is computed based on

optic disc (OD) center and its radius. We have devel-

oped an OD detection and center computation method

which is reported in (Bhuiyan et al, 2009).

2.2 Edge Tracking from Zone B Area

Once we compute the zone B area, we track the in-

dividual edges for proﬁling and ﬁltering to ﬁnd the

actual vessel edges. From the zone B area, we scan

the canny edge detected image and track each of the

individual edges and corresponding pixels by region

growing technique (Gonzalez and Woods, 2008).

2.3 Edge Reconstruction

Because of the central reﬂex properties it may be pos-

sible that a central reﬂex edge merge with a vessel

edge (Figure 3) or with another central reﬂex edge.

This situation will create noisy proﬁle for a real ves-

sel edge. In order to overcome this situation, once

we trace the edges individually, we check if there is

any edge which merges with another edge. We check

each edge if there is any junction point (which is a

merging point of 3 edge-segments) within the edge

points. Once we recognize this instance, we map the

vessel segments for this point. We measure the slope

of the segments locally by considering this point and

another short distance point in each of the segments.

From this, we select the two edge segments which

have the closest slope and discard the other segment.

2.4 Edge Proﬁling

The edge proﬁling method ﬁlters out the noise and

backgrounds, and selects the edges which have more

likelihood of vessel edges. The method checks the

intensity levels on both sides of an edge within a spe-

ciﬁc direction. For this, each of the edge pixels is con-

sidered to obtain two pixels’ positions (on both sides

of the edge pixel) which are located at a certain nor-

mal distance from the edge pixel. For this, each pixel

along with its neighboring pixel in the edge are con-

sidered as the line end-points. The slope and actual

direction of the line are computed to ﬁnd the points

on both sides of the current edge pixel. The method is

as follows.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

468

Let us consider the two end-points of the line are

) and (x

), and the angle θ (actual angle in

the image) is computed from the slope and direction

of the line which are slope and direction. Let us as-

sume that (x

) is the second end-point i.e., located

further from the OD compared to the ﬁrst end point;

we ﬁnd the value of θ as follows. If θ < 0 and if

≥ y

& x

≥ x

then θ = θ+ π. On the other hand,

if θ < 0 and if y

≤ y

& x

≤ x

then θ = θ + 2π.

If θ > 0 and if y

≥ y

& x

≤ x

then θ = θ + π.

Once the actual angle is computed, the point located

on left side of the edge point (x

) is computed as:

((y

− r ∗ sin(θ + π/2)),(x

+ r ∗ cos(θ + π/2)) and

the point on right side of the edge point is: ((y

− r∗

sin(θ+3π/2)),(x

+r∗cos(θ+3π/2)) where r is the

normal distance from the point (x

After computing the pixel positions on both sides

of each of the edge points, the intensity levels for

these position in the image are obtained. For this,

we consider the Green channel image after Gaus-

sian smoothing (Gonzalez and Woods, 2008). Usual

vessel edge proﬁle is high-to-low for the outside-to-

inside pixels’ intensity levels and low-to-high for the

inside-to-outside pixels’ intensity levels. For blood

vessels, this proﬁle is consistent, whereas for noise,

this proﬁle is random. Therefore, the consistent pro-

ﬁle value for each of the potential edges is considered

for ﬁltering the true vessel edges and for discarding

the noisy edges. Figure 3 shows the traced edges and

the edges obtained from edge proﬁling method.

2.5 Potential Vessel Edge and Position

Identiﬁcation

After proﬁling the edges the length of an edge is also

computed to check if it passes a certain threshold

value for a vessel edge. Then an edge is deﬁned as the

ﬁrst edge of a vessel if it returns the proﬁle value for

high-to-low. Similarly, the edge is deﬁned as the sec-

ond edge if the proﬁle value is low-to-high. Following

this, we check if there is any disjointed edge by ex-

tending the edge based on extrapolation into a cer-

tain distance. To control the direction of the exten-

sion points, the edge slope is computed by local edge

points through the edge end point and another point

within 5 pixel distance from it. We merge the edges

once the edge type matches. Following this, we sort

the edge position to combine the edges as vessel edge.

The edge positions are sorted based on angular posi-

tion of each of the edges around the OD. We consider

the OD center and edge start point (closer end point

of the edge from OD) to compute the angle. Based on

the position we consider each of the edges to merge

as vessel edge.

(a)

(b)

Figure 3: Image showing the detected edges of noise and

central reﬂexes (left), and detected actual edges after proﬁl-

ing (right).

2.6 Vessel Identiﬁcation and Centerline

Detection

For merging the vessel edges we check the sorted

edges and consider the ﬁrst edge and second edge in

a sequence and within a certain distance. Generally,

after applying the edge detection, two edges are ob-

tained for any blood vessel if there is no central reﬂex.

If there is a central reﬂex in the vessel, it may be two

or three or four edges based on the intensity levels of

the central reﬂex edges. We consider this information

for merging edges into blood vessels.

In general, the central reﬂex is approximately 1/3

of the vessel width (Kaushik et al., 2007). Consider-

ing this, we merge the edge labeled as ﬁrst and sec-

ond edge if there is no other ﬁrst or second or ﬁrst-

second combination within approximately same dis-

tance. The distance is measured as the Euclidian dis-

tance between the two edge start-points. If we have

ﬁrst-ﬁrst-second combination of the edges, we check

the overall distance between the ﬁrst and last edge,

and between the middle and last edge. If the condi-

tions satisfy the edges to be part of a vessel, we de-

ﬁne the edges belong to an individual vessel. Similar

approach is applied for ﬁrst-second-second combina-

tion. For the ﬁrst-second-ﬁrst-second combination we

check all the distances; the ﬁrst ﬁrst-second pair, the

second ﬁrst-second pair, the second-ﬁrst (i.e., the sec-

ond and third edge which is the width of the central

reﬂex) and the ﬁrst and last edge pair (i.e., the width

of that cross-section). If these distances satisfy the

vessel edge-central reﬂex properties, we deﬁne these

as a single vessel. Otherwise, the ﬁrst ﬁrst-second

A NEW AND EFFICIENT VESSEL SEGMENTATION METHOD FROM COLOR RETINAL IMAGES

469

Table 1: Results of edge start-point detection.

Large Vessels Small Vessels

Total Number (manual) 292 164

Automatic Detection 270 116

Accuracy 92.47% 70.73%

edge pair is deﬁned as one vessel and the second ﬁrst-

second starts to compute the next vessel edge merging

process. Figure 4 and 5 shows the output images for

vessel detection in different circumstances. Once we

deﬁne the vessel and its edges we then track the cen-

terline from averaging the edge-pixels’ locations.

3 EXPERIMENTAL RESULTS

AND DISCUSSION

We analyzed ﬁfteen retinal images obtained from the

Singapore Malay Eye Study (SiMES) dataset (Wong

et al., 2004c) which are randomly selected by reti-

nal image graders and applied our method to pro-

duce the output images indicating the detected ves-

sels. Each image size of 2048x3072 was taken from

using a digital non-mydriatic retinal camera (Canon

CR-DGi with a 10D SLR backing). It has taken ap-

proximately 8.47 seconds on MATLAB 7.8 to pro-

duce each output image on a 2.53 GHz Pentium(R) 4

CPU with 3.2 GB of RAM. Experimental results of

the accuracy of starting point detection are shown in

Table 1. Large vessels are deﬁned as those vessels

which are more than 45 µm in diameter. We note that

we compute the vessel diameter in microns by consid-

ering the distance between the pixels in microns (from

calibrating the camera). After computing the vessel

diameter in microns, those vessels with diameter less

than 45 µm are deﬁned as small vessels and are ig-

nored for the CVD prediction (Hubbard et al., 1999).

We observe that vessel start-point detection accuracy

is 92.47% for large vessels and overall vessel start-

point (i.e., both major and minor) detection accuracy

is 81.60%. Based on these start-points, overall large

vessel detection accuracy is 88.73% (Table 2).

Table 2: Results of Vessel Tracking.

Number of Vessels

Actual 142

Detected 126

Accuracy 88.73%

(a)

(b)

Figure 4: Image showing the detected edges with noise and

central reﬂexes (left), and detected vessels (right).

(a) (b)

Figure 5: Cropped image showing detected vessel and cen-

tral reﬂex edges (a) and (c), and their corresponding de-

tected vessel images (b) and (d). Image showing correctly

detected vessels within the positions of two side-by-side

vessels with central reﬂexes (b).

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

470

4 CONCLUSIONS AND FUTURE

WORK

In this paper, we discussed a novel method for de-

tection of blood vessels from color retinal images.

Our method is highly accurate in detecting blood ves-

sels with central reﬂex. The novelty of our method

lies in the detection of blood vessels with vessel and

central reﬂex edge tracking with varying contrast. A

user friendly interface is developed to select the miss-

ing start points and to identify the missing vessels

from which the width will be measured automatically.

Based on the method, we are in the process of devel-

oping a new technique for vessel width measurement.

The results for vessel width measurement and CVD

prediction model will be reported later.

ACKNOWLEDGEMENTS

The research has been supported by National Health

and Medical Research Council (NHMRC) Australia

development grant (2008).

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