A SIMPLE ANALYTIC APPROACH FOR TRACKING RETINAL

VESSELS AND MEASURING THEIR DIAMETERS

Zafer Yavuz, Cevat Ikibas and Cemal Kose

Dept. of Computer Engineering, Karadeniz Technical University, Trabzon, Turkey

Keywords: Retinal image processing, Vessel diameter, Retinal vessel tracking, Automatic measurement and tracking,

Thinning algorithm.

Abstract: Retinal image processing provides tools for automatic diagnosis and monitoring of retinal diseases such as

diabetic retinopathy (DR), age related macular degeneration (ARMD), glucoma and such. The properties of

vessel structures on the other hand are widely utilized in locating morphologic structures such as optic disc

and macula and in automatic diagnosis of the retinal diseases. Due to the importance of retinal vessels, we

propose a simple approach for vessel tracking and measuring vessel diameter in retinal fundus images.

Images having manually segmented retinal vasculatures are obtained from STARE database and used in this

study. Our method first finds the midlines of the vessel network on the segmented images by employing

Zhang-Suen thinning algorithm and then tracks the vessel branches through those midlines. Lastly, the

diameters of the vessel segments in different parts of the vasculature are calculated along with the tracking

operation. The performed test results show that the proposed automatic method is quite successfully tracks

the vessel network and measure the diameter.

1 INTRODUCTION

Along with the advancements in technology, the

number and capability of techniques used in medical

fields are increased. For example, automated image

analysis and processing is one of the most promising

areas of computer vision used in medical diagnosis

and treatment. In this context, retinal fundus images

offering very high resolutions that are sufficient for

most of the clinical cases provide many indications

that could be exploited in diagnosing and screening

retinal degenerations or diseases (Köse, 2006),

(Köse and İkibaş, 2008). As a matter of fact, using

modern image processing techniques in

ophthalmology gained significant interest especially

in the last 15 years. The developments include

automated diagnosing and monitoring systems for

conditions such as degenerations, DR, ARMD etc,

and detection of retinal landmarks such as optic disc,

vascular network, macula and such (Köse et al.,

2009), (Köse et al., 2008). The automated tools in

ophthalmology have significant contributions in that

they offer a great potential to be used in operations

on large data set, which requires a substantial trained

human effort when they are manually processed.

Using these tools could indeed save a lot of

resources, and they are free from environmental

effects, graders’ bias and fatigue, and image quality.

Therefore, the increasing demand for these kinds of

tools and techniques will sure continue in the future.

Since retinal vessels are usually affected by the

existing diseases in retina (American Academy of

Ophthalmology, 1991), the diseases can be analyzed

and diagnosed by measuring parameters such as

vessel diameters, branch angles and lengths (Stanton

et al., 1995), (Hutchins et al., 1976). On the other

hand measuring vessel diameter is not an easy task

because of variation in vessel morphology and the

quality of retinal images.

Even though quite number of methods proposed

for measurement of vessel diameters, those methods

mostly deal with not the direct measurement of the

diameter but improving the accuracy of edge

location. On the other hand, the measurement of

parameters varies depending on the type of

instruments used to record the vessel profile and the

experience of the professional. The process is also

very laborious and time consuming. Even though

some improvement are done in estimating the vessel

diameter by single Gaussian function modelling the

vessel intensity profile (Newsom et al. 1992), the

Yavuz Z., Ikibas C. and Kose C. (2010).

A SIMPLE ANALYTIC APPROACH FOR TRACKING RETINAL VESSELS AND MEASURING THEIR DIAMETERS.

In Proceedings of the First International Conference on Bioinformatics, pages 13-18

DOI: 10.5220/0002694200130018

 SciTePress

Gaussian model could fail in estimating diameter

since high resolution fundus photographs often

display a central light reflex (Gao et al., 2000).

Pappas et al. estimated the vessel diameter with

an elliptical vessel profile and used a second order

model for the background (Pappas and Lim, 1998).

A method is presented by Gao et al. to model the

intensity profiles over vessel cross-section using

twin Gaussian functions to acquire adequate

information for subsequent image characterization,

leading to the development of automatic

measurement system for retinal images. This method

develops simple relationships between vessel width

and the intensity distribution parameters, which

provides robust estimators of vessel width even in

the presence of image noise, and varying

background intensities (Gao et al., 2001). A method

of semi-automated image analysis for the

measurement of retinal vessel diameters is described

in (Newsom et al. 1992). The technique was

compared with an observer-driven method for

reproducibility and accuracy. A computerized

system is presented for the automatic quantification

of blood vessel topography. This system applies

strategies and algorithms for measuring vascular

trees and includes methods for locating the centre of

a bifurcation, detecting vessel branches, estimating

vessel diameter, and calculating angular geometry at

a bifurcation. But the system had difficulty dealing

with very noisy images and small or especially

tortuous blood vessels (Gao et al., 2000). Another

study presents an algorithm measuring the vessel

diameter based on a two-dimensional difference of

Gaussian model, which is optimized to fit a two-

dimensional intensity vessel segment (Lowell et al.,

2004). Although these methods work fine in healthy

images, they fail in some cases such as pathological

structure and low image quality. This study suggests

a simple alternative method contributing to the

automatic retinal image analysis. The developed

method successfully tracks the vessel network and

measure vessel diameters.

Figure 1: A sample manually segmented vessel network

taken from STARE project.

Figure 2: (a) Manually segmented retinal vessel network,

(b) Skeleton of (a) generated using Zhang-Suen thinning

algorithm

The rest of the paper is organized as the

following. In Section 2, we will give the algorithm

development for vessel tracking and measuring the

vessel diameter. Section 3 explains the test results

on manually segmented retinal images obtained

from STARE project. Conclusions and future works

are given in Section 4.

2 ALGORITHM DEVELOPMENT

This study introduces an algorithm for tracking

vessels and measuring their diameters. The

suggested method is applied on manually segmented

images obtained from STARE project website. The

STARE project includes detailed measurements of

the anatomical structures and lesions visible in the

retinal images. Here, our method first applies a

thinning algorithm on those images to get midlines

of the vessels, and then the vessels are tracked and

their diameters are measured.

2.1 Vessel Segmentation

Vessel segmentation is the first phase to track the

vessel network. Since our method does not have any

tool for segmentation, we directly used the retinal

images with manually segmented retinal vessels

obtained from STARE project web site

(http://www.parl.clemson.edu/stare/, 20.03.2009).

An example of segmented binary vessel network

image is given in Figure 1.

2.2 Skeletonization and Thinning

Skeletonization is a way of describing the global

properties of objects and representing the original

images more compactly. The skeleton, constructed

with the width of one pixel, shows the structural

connectivity of the main components of an object.

Skeletonization techniques are applied in quite

number of different areas such as character

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Figure 3: Removed junctions and generated sample vessel

segments.

recognition problems and extracting vessel topology

in retinal images.

Thinning is one of the basic methods for

skeletonization. As a result of this iterative

technique, the skeleton of vessel network is

extracted. According to this technique, the edge

pixels having at least one adjacent background point

are removed from the vessel in every iteration. All

of these kinds of pixels can be deleted, if their

removal doesn't affect the topology of the vessel

network. Here, the generated skeleton represents the

shape of the vessel network with fewer pixels as in

Figure 2.b (Ritter and Wilson, 1996).

The Zhang-Suen Thinning Algorithm is

employed in this study (Ritter and Wilson, 1996),

(Russ, 1992). In this skeletonization algorithm, the

new value is obtained based on the previous iteration

in a parallel manner.

The algorithm is finalized if no more change is

needed at the end of either sub iteration. Figure 2

shows an example of the thinning process produced

by using Zhang-Suen Algorithm.

2.3 Obtaining Vessel Segments

After the thinning operation, the crossing points of

all vessel networks are obtained. According to

crossing points, the vessel network is divided into

segments and each segment is labelled with different

numbers. A small part of sample retinal vessel

network separated in to segments is shown in Figure

Relatively small vessel segments are omitted if

their length is less than 8 pixels. At the end of these

operations, vessel segments are ready for tracking

and measuring their diameters, which is explained in

the following section in detail.

2.4 The Vessel Tracking Method

Vessel tracking method is an important phase of

measurement of vessel diameter since our algorithm

tracks the vessel midline points while measuring the

thickness of the vessel segments in different parts of

the network.

According to our algorithm, consecutive points

on vessel midlines are considered in tracking. In

each step of tracking, based on the location of

current point, direction vector is determined to be

the vector connecting P

c-2

and P

c+2.

Even though the

direction vector could have been simply defined

based on the difference of the first and last point of

currently processed points, the average of the

vectors in Figure 5.a and b better represent the

direction of current point. Here, the direction vector

is calculated considering five points, two of which

are located before the current point and the other two

are located after the current point. Totally five points

are processed in determination of vessel direction for

the current point, which is explained in Section 2.5

in details. The tracking method goes on until the end

point is reached as shown in Figure 4.

Figure 4: The midlines of vessel network utilized in

tracking and measuring the vessel diameters.

Figure 5: Graphical representation of (a) difference

vectors and (b) the calculated direction vector based on

difference vectors.

A SIMPLE ANALYTIC APPROACH FOR TRACKING RETINAL VESSELS AND MEASURING THEIR

DIAMETERS

2.5 Measuring the Vessel Diameter

So far, the operations to obtain points on midlines of

the whole vessel network are performed. The next

steps include calculation of the direction vectors and

measuring the vessel diameters.

In order to track the vessel branches, we first

calculate the direction vector for each point on the

midline produced using thinning algorithm. Here the

direction vector is calculated as the average of five

vectors. Relative to the current point P

, the five

points are P

c-2

, P

c-1

, P

c+1

, P

c + 2

. Each point has

axis and ordinate called x and y. The differences

between the first point 



and all other points

represent different vectors as shown in Figure 6.a.

The result vector representing the direction for the

current point is shown in Equation 1.





































(1) α + β = χ. (1) (1)

where 







,







,







,and 







represent the vectors

based on the difference between each point P

c-1

, P

c+1

, P

c + 2

and the first point P

c-2

. The formula for

calculation of these vectors is given in Equation 2.















(2)

where  is 1, 2, 3 and 4. This operation is conducted

for each point on the vessel midlines. The calculated

vector is then normalized and orthogonal vectors

used in measuring the vessel diameters are found for

the current point.

Given the direction vector 







, 



, the

orthogonal vectors 



and 



are calculated to be









,



 and 







,



. These

orthogonal vectors are shown in Figure 7.

Measuring the diameter of vessels is done

considering the direction vector. For each point

through the midlines, two orthogonal vectors to the

direction vector are calculated. The diameter for the

current point is calculated to be the addition of

distances starting from the current point and ending

in vessel edges through these orthogonal vectors in

both directions as depicted in Figure 6. Here, the

diameter is the total length of 



and 



which are

orthogonal vectors to the direction of the current

point. The vectors used in calculations are all

normalized vectors.

Figure 6: The orthogonal vectors calculated for each pixel

through the midlines of vessel network, and measuring the

vessel diameter.

Figure 7: An example of manually generated test vessel

segment.

Figure 8: The calculated diameter of different parts of test

vessel segment in Figure 7.

3 RESULTS

The proposed methods for tracking retinal vessels

and measuring their diameter are tested on the

images with manually segmented retinal vessels

obtained from STARE project web site

(http://www.parl.clemson.edu/stare/, 20.03.2009). It

is illustrated in Figure 7 and 8 that the proposed

methods successfully track the vessel network and

measure the diameter of vessels in retinal images.

The calculated diameters of a vessel segment are

BIOINFORMATICS 2010 - International Conference on Bioinformatics

also depicted in a graph in Figure 8. A real vessel

network is also processed to show the vessel

structure and calculate the diameters in different part

of vessel network. One example of this covering a

small part of the whole network is illustrated in

Figure 9. The diameter of real vessel segment

shown in Figure 9 is also graphically presented in

Figure 10. These figures show that the graphical

representation of the size of diameters is perfectly

understandable and give ideas about the gradual

change in diameters of vessels. The graphical

representation can be a visual tool for

ophthalmologists to see if there is any sudden

change in vessel diameter, which could be a sign of

different retinal diseases such as lesion and

haemorrhages, bleeding etc.

In Figure 8, the x axis shows in which part of the

vessel of Figure 7 the diameter is measured, and y

axis shows the calculated diameter on that point. As

seen in the figure, the diameter of vessel decreases

through the vessel segment which is correct for the

processed vessel. Figure 9 and 10 also shows an

original retinal image, the extracted vessel segment

from the image, and the diameter calculated on the

extracted vessel segment. y and x axis show the

calculated diameter and on which point of the vessel

segment the diameter is calculated. Manual

calculations of vessel diameters in some points also

show that automatic and manual measurements

throughout the processed vessel segment are very

close to each other.

Overall, the suggested method successfully

tracks the vessels and measures their diameters

through the vessel segment. Here it can be stated

that the method is successful in tracking and

measurement in all kinds of retinal vessels.

Figure 9: A retinal fundus image and a small part of the

real retinal vessel network.

Figure 10: Diameters of vessel shown in Figure 9.

4 CONCLUSIONS

Tracking vessels network in retinal images and

measuring the diameters are very important since the

morphological changes in vessel structures can be a

sign of different retinal diseases. For example

sudden diameter changes can be a sign of

haemorrhages or bleeding area. The vessel diameters

can also be utilized in verification of vessel

segmentation. Since the diameter of vessel structure

gradually decreases while moving away from optic

disc region, the sudden changes in diameters of

segmented vessels would be a sign of inaccurate

segmentation if the retinal image does not contain

any disease. The direction vector would also be

employed in the same way. The direction vectors of

branches in vessel structure are supposed to be in

similar direction with the main root vessel from

which the branches originate. Especially in junctions

of vessel networks, illogical direction vector of

branches would be a sign of erroneous segmentation.

In addition to graphical representation, vessel

colorization based on the diameter of vessels can be

done as a feature work of this study for a better

representation. This could also be a visual tool for

ophthalmologist to visually detect the retinal

diseases, the major sign of which is the changes in

diameters.

REFERENCES

American Academy of Ophthalmology, (1991).

Ophthalmic Pathology, (Section 11, pp 179), Basic

and Clinical Science Courses.

Gao, X. W., Bharath, A., Stanton, A., Hughes, A.,

Chapman, N., Thom, S., (2000). Quantification and

Characterisation of Arteries in Retinal Images. (Vol.

63, Num. 2, pp. 133-146(14)), Computer Methods and

Programs in Biomedicine.

Gao, X, Bharath, A, Stanton, A, Hughes, A, Chapman,

Thom, (2001). Measurement of Vessel Diameters on

Retinal Images for Cardiovascular Studies. On-line

Conference Proceedings: Medical Image

Understanding and Analysis.

Hutchins, G.M., Miner, M.M., Boitnott., J.K., (1976).

Vessel Calibre and Branch-Angle of Human Coronary

A SIMPLE ANALYTIC APPROACH FOR TRACKING RETINAL VESSELS AND MEASURING THEIR

DIAMETERS

Artery Branch-Points, (Vol. 38, pp 572), Circulation

Research.

Köse, C., (2006). Fully Automatic Segmentation of

Coronary Vessel Structures in Poor Quality X-ray

Angiograms Images, (Vol. LNCS 4109, pp. 72-82),

Springer: Lecture Notes in Compute Science.

Köse, C., and İkibaş, C., (2008). Segmentation of

Coronary Vessel Structures in X-ray Angiogram

Images by Using Spatial Pattern Matching Method,

(pp. 1-6), ISCIS2008.

Köse, C., Şevik, U., and Gençalioğlu, O., (2008).

Automatic segmentation of age-related macular

degeneration in retina fundus images, (Vol. 38, pp.

611-619), Computers in Biology and Medicine.

Köse, C., Şevik, U., Gençalioğlu, O., İkibaş, C., and

Kayıkçıoğlu, T., (2008). A Statistical Segmentation

Method for Measuring Age-Related Macular

Degeneration in Retinal Fundus Images, Journal of

Medical Systems. doi: 10.1007/s10916-008-9210-4.

Köse, C., Gençalioğlu, O., and Şevik, U., (2009). An

Automatic Diagnosis Method for the Knee Meniscus

Tears in MR Images, (Vol. 36, pp. 1208-1216), Expert

System With Applications.

Lowell, J, Hunter, A, Steel, D, Basu, A, Ryder, R,

Kennedy, (2004). Measurement of Retinal Vessel

Widths from Fundus Images Based on 2D Modeling,

Ieee Transactions On Medical Imaging.

Martin, A., Tosunoglu, S., (2000). Image Processing

Techniques for Machine Vision, Florida Conference

on Recent Advances in Robotics, Boca Raton, FL:

Florida Atlantic University.

Newsom, R. S. B., Sullivan, P. M., Rassam, S. M. B.,

Jagoe R., Kohner, E. M., (1992). Retinal Vessel

Measurement: Comparison between Observer and

Computer Driven Methods, (Vol. 230, pp. 221-225),

Graefe's archive for clinical and experimental

ophthalmology.

Pappas, T.N., and Lim, J.S., (1988). A New Method for

Estimation of Coronary Artery Dimensions in

hgiograms, (Vol. 36, pp. 1501-1512). IEEE Trans. On

Acoust. Sp. And Sign. Proc.

Parker, J., R., (1994). Practical Computer Vision using

C, Wiley Computer Publishing.

Ritter, G., X., Wilson, J., N., (1996). Handbook of

Computer Vision Algorithms in Image Algebra, CRC

Press.

Russ, J. C., (1992). The Image Processing Handbook,

CRC Press

Sonka, M., Hlavac, V., Boyle, R., (1998). Image

Processing, Analysis, and Machine Vision, 2nd

Edition, Pws. Pub. Co.

Stanton, A.V., Wasan, B., Cerutti, A., Ford, S., Marsh, R.,

Sever, P.P., Thom, S.A., Hughes, A.D., (1995).

Vascular Network Changes in the Retinal with Age

and Hypertension, (Vol.13, pp 1724), J. Hypertens.

STARE Project, Retrieved 20 March 2009 from

http://www.parl.clemson.edu/stare/.

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