AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING

USING VASCULAR FEATURES

Alauddin Bhuiyan

1,2

, Baikunth Nath

, Kotagiri Ramamohanarao

and Tien Y. Wong

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

Victoria 3010, Australia

Centre for Eye Research Australia, The University of Melbourne Australia, Victoria 3002, Australia

Keywords:

Retinal image, Vessel bifurcation, Branch and crossover points, Invariant feature, Feature vector, Binary tree,

Tree matching algorithm.

Abstract:

In this paper, we propose a method for retinal image matching that can be used in image matching for person

identiﬁcation or patient longitudinal study. Vascular invariant features are extracted from the retinal image and

a feature vector is constructed for each of the vessel-segments in the retinal blood vessels. The feature vectors

are represented in a tree structure with maintaining the vessel-segments actual hierarchical positions. Using

these feature vectors, corresponding images are matched. An image matching method is demonstrated which

identiﬁes the same vessel in the corresponding images for comparing the desired feature(s). Initial results

demonstrate that the method is suitable for image matching and patient longitudinal study.

1 INTRODUCTION

Recent advancement in retinal imaging has enable us

to use images for earlier diagnosis of diseases, image

matching in biometric security application and infor-

mation retrieval purposes. Studies show that changes

in the retinal vascular features such as vessel width,

tortuosity and branching angle are very important in-

dicator for predicting hypertension and cardiovascu-

lar diseases (Wong et al., 2006),(Wong et al., 2003).

In addition, retinal vascular pattern is unique to each

individual and can be used in image matching appli-

cations such as biometric authentication and informa-

tion retrieval. Hence, a suitable approach that can ac-

curately analyze retinal images for both disease di-

agnosis and image matching purposes will be a very

signiﬁcant contribution in this image modality.

For disease prediction or clinical trial, the most

widely used approach is to take person’s retinal im-

age (Figure 1(a)) within a time interval and com-

pare these images to observe the change(s) in the vas-

cular features (Stanford et al., 1988),(Abrahams and

Gregerson, 1983). In various studies (Hughes et al.,

2008),(Taarnhoj et al., 2008) authors have reported

the effect of hypertension treatment on retinal vessel

diameter and tortuosity which is performed by tak-

ing person’s retinal image before and after medica-

tion. These studies mainly consider manual or semi-

automatic methods for image analysis which are very

time consuming and expensive. Further, these studies

are based on analyzing a single feature which is not

enough to observe multiple feature changes. None of

these techniques is able to match the vascular features

from two images based on vessel-segments hierarchi-

cal position, which is very important for automated

patient longitudinal study.

Retinal image matching methods for person iden-

tiﬁcation mainly register images after vessel segmen-

tation, or match bifurcation or branch point in the cor-

responding images (Marino et al., 2006), (Harris and

Yen, 2002), (Womack, 1994). In the ﬁrst approach,

retinal blood vessels are used as the biometric param-

eter, with a prior registration stage needed to align the

template image and the acquired image. The second

approach segments the blood vessel from the image

and computes the bifurcation/branch and crossover

points (Figure 1(b)). Overall, bifurcation/branch and

crossover points geometry should provide higher in

generating unique pattern for an individual person. At

present, retinal vascular bifurcation and branch points

are consider as the same parameter for image match-

ing applications. Classiﬁed bifurcation and branch

points can add higher degree in the uniqueness of the

retinal vascular pattern.

Considering these issues, we propose an auto-

matic retinal image matching method, which is highly

342

Bhuiyan A., Nath B., Ramamohanarao K. and Y. Wong T..

AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING USING VASCULAR FEATURES.

DOI: 10.5220/0003275903420347

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 342-347

ISBN: 978-989-8425-35-5

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

(a) (b)

Figure 1: A retinal image showing the blood vessels (a) and a cropped section showing vascular Bifurcation (circle), Branch

(square) and Crossover (star) points(b).

potential for patient longitudinal study and biometric

security application. The method uses retinal vascular

bifurcation, branch and crossover points as features

to match the images. We utilize the retinal vessel

hierarchical property and construct a binary tree for

each vessel to match images with high conﬁdence. A

number of vessel tree construction methods are avail-

able in the literature for retinal image (Deng et al.,

2010),(Lin et al., 2009),(Guo et al., 2006). None

of them represents multiple features for each of the

vessel-segments hierarchically. Further, no method

classiﬁes the bifurcation and branch points to match

the images which provides higher degree of unique-

ness in the pattern. Our proposed method will be

able to match retinal images for authentication or ana-

lyzing multiple feature changes for disease diagnosis

purposes.

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

tion 2 describes the proposed method which includes

the vascular tree-model construction, invariant feature

extraction and tree matching based on the invariant

features. Initial results on the matching method which

can be used for patient longitudinal study is shown in

section 3. Section 4 concludes the paper with future

work.

2 PROPOSED METHOD

Our proposed image matching method is based on

the tree representation of the retinal vascular network

which is a hierarchical representation of features of

the vessel-segments of blood vessels. In this pa-

per, we mainly focus on the vascular invariant fea-

ture extraction, the feature vector construction and the

matching algorithm for patient longitudinal study. We

utilize retinal bifurcation, branch and crossover points

to construct the feature vector. The vascular invariant

features along with the vessel-segments hierarchical

position provide a unique pattern for each of the blood

vessels in the retina for each individual person.

2.1 The Tree-model

We have applied a method for developing a tree struc-

ture (we call it tree-model) of the retinal vascular net-

work which is based on vessel centerline (L), width

(W) and bifurcation (B

), branch (B

) or crossover

) point information in the retinal optical fundus

image as represented in Figure 2(a). We have ap-

plied the methods for vessel segmentation and cen-

terline detection (Bhuiyan et al., 2007a) and bifurca-

tion, branch and crossover points detection (Bhuiyan

et al., 2007b) which will be used in the tree-model.

The construction of the tree-model begins by tracking

the vessel centerline from the border of the optic disc

(OD) in the retinal image. This corresponds to the

root node in the tree-model. Each vessel originating

from the optic disc is then represented by a binary tree

and is linked to the root node in the tree-model.

The blood vessel centerlines are fragmented into

different vessel-segments based on the bifurcation,

branch and crossover points. Initially, these landmark

points and their corresponding vessels’ start or end

points are computed and stored. Hence, a vessel-

segment starts from the optic disc boundary or a bi-

furcation or a branch point, and ends at a bifurcation

or branch point. For each vessel-segment, the features

are computed and inserted into its corresponding node

in the tree-model. This is shown in Figure 2 for vessel

Vessel-segment V

(starts from S

and ends at

) is traced, and its features are inserted into the

corresponding node (node V

in Figure 2(b)) in

the tree-model. Its daughter vessel-segments V

and V

are then traced. These vessel-segments’

features are inserted into the tree-model as children

nodes of V

(Figure 2(b)). Similarly, vessel-

segments V

and V

are inserted as the children

node of V

. Generally, a vessel-segment appears

as two parts in a crossover point (C

in Figure 2(a)).

Therefore, the crossover point is used to trace the

other part of a vessel-segment. For example, as in Fig-

ure 2(a), when the crossover point C

is encountered,

AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING USING VASCULAR FEATURES

343

(a) (b)

Figure 2: A schematic diagram of a retinal image showing two blood vessels (V

and V

) and the optic disc (a) and the

tree-model representing the binary tree for vessel V

(b).

the end point of the vessel segment V

is measured

as E

instead of E

′

2.2 Invariant Features for Image

Matching

For image matching we consider the vascular in-

variant features i.e., the features invariant to rota-

tion, translation or scaling. The features are: vessel-

segment’s length to width ratio (L/W), bifurcation or

branch point, Information on crossover existence in

a vessel-segment, crossover location in the vessel-

segment and acute angle between the parent and

smaller daughter vessel-segments. We consider these

invariant features which provide a unique pattern for

any individual’s retina. For each vessel-segment these

feature values are inserted into the corresponding

node in the tree-model.

2.3 Vascular Feature Extraction

For vascular feature extraction we consider vessel

centerline image, edge image and fragmented center-

line image. The methods for obtaining these images

are described in (Bhuiyan et al., 2008).

Length to Width Ratio (L/W). We implement region

growing algorithm (Gonzalez and Woods, 2008) to

extract the length of a vessel-segment. Using the op-

tic disc center and radius, we search a circular region

around the OD and determine the starting pixel of a

vessel centerline. Once a vessel centerline (starting)

pixel is obtained we traverse through the centerline

until we reach its end point.

For each vessel-segment, the starting pixel is con-

sidered for initiating the traversal process in the frag-

mented vessel centerline image. The traversal algo-

rithm uses the 3× 3 neighborhood connectivity check

to ﬁnd the neighboring pixels in the vessel-segment.

For this, a mask is applied by considering the start-

ing pixel of any vessel-segment as its center. Once

a neighboring pixel is found it replaces the previous

center pixel in the mask. Each time a pixel position

is considered, a ﬂag value is assigned to this. Once

the traversal process reaches the vessel-segments end

point, it stops if it belongs to a bifurcation or a branch.

The traversalprocess also stops if the vessel-segments

end point is not obtained after a certain number of

iterations. Once the end point is obtained, the to-

tal length of this vessel-segment is returned in pixel

number and average width (Bhuiyan et al., 2008) is

computed. These values are inserted into the corre-

sponding node in the tree-model.

Bifurcation or Branch Point. We classify each land-

mark as bifurcation or branch point (Bi/Br). The bi-

furcation, branch and crossover point classiﬁcation

method is applied by using the method described in

(Bhuiyan, 2009). We assign a unique value (1 for bi-

furcation and 0 for branch) in the feature value for

each of the vessel-segment.

Existing Crossover. For each vessel-segment if there

is any crossover point exists we assign a unique value

for the node. We insert 1 for presence of a crossover

point and 0 for absence of the crossover point to the

node value.

Crossover Location. For the vessel-segment, if there

is any existing crossover point we assign the posi-

tional information of that crossover. For example, if

the actual length is 100 pixels and the crossover po-

sition is on the 70

pixels from the starting of the

vessel-segment; we assign the ratio of its position to

the total segment length, which is 0.7. If there is no

crossover we assign 0 for this feature value.

Acute Angle between the Parent and Smaller

Daughter Vessel. We ﬁnd the acute angle between

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

344

Figure 3: Vascular widths (left) and Angles (right).

the parent and the smallest vessel. The parent and

smallest (daughter) vessel is classiﬁed by using the

width of each of the vessels for any bifurcation or

branch point. The vessel-segments for each bifurca-

tion or branch point are sorted based on their width.

Then the angle between the centerlines of highest and

lowest width vessels’ are computed. Figure 3 shows

the vessel-segments width and the corresponding an-

gles.

2.4 Accessing Vascular Features from

the Tree-model

We access the vascular features from the tree-model

by using preorder tree traversal algorithm (Knuth,

1973). To traverse a non-empty binary tree in pre-

order, we perform the following operations recur-

sively at each node. Starting at the root of a binary

tree, we retrieve the values for the current node (re-

ferred to as ”visiting” the node), then traverse to the

left child node, and ﬁnally traverse to the right child

node.

2.5 Feature Vector Construction and

Matching

Each binary tree is traversed and the invariant feature

values (in Table 1) are obtained from each node. Fol-

lowing this, for each node (i.e., each vessel-segment)

we construct a feature vector from these invariant fea-

ture values. We note that the invariant features with

their actual hierarchical position in the tree-model en-

hance the uniqueness of a feature vector. We then,

compare the corresponding feature vectors on the tree

traversal order for ﬁnding the binary trees (which rep-

resent the same vessel) from the tree-models.

For image matching, our goal is to ﬁnd the cor-

responding binary trees in the tree-models that repre-

sent the same vessel in the two retinal images. We

perform the bitwise comparison of the feature vec-

tors to ﬁnd the distance between the corresponding

binary trees. For each corresponding feature vector,

if a match is found, we add zero to the ﬁnal matching

Table 1: The invariant features for vessel-segments in the

nodes of a tree-model.

Invariant Features

Nodes L/W Bi/Br Crossover Cross pos. Acute angle

1 88.77 1 0 0 0.877

2 18.27 0 0 0 1.126

3 43.12 0 0 0 0

4 34.27 1 1 0.79 0.788

5 53.42 1 0 0 0.689

Table 2: The distance matrix between the vessels for an

image.

Distance between the vessels in node number

Vessel1 Vessel2 Vessel3 Vessel4 Vessel5

Vessel1 0 7 7 7 7

Vessel2 7 0 7 7 7

Vessel3 7 7 0 7 7

Vessel4 7 7 7 0 7

Vessel5 7 7 7 7 0

distance and one if there is no match. We note that the

matching distance is the distance between the binary

trees (i.e., the vessels) in node number. We obtained

the matching matrix by comparing the corresponding

binary trees in the tree-models.

For ﬁnding the corresponding binary trees, we

consider the upper two levels’ nodes from each of the

binary trees. This way we are able to obtain sufﬁcient

number of feature vectors for matching to ﬁnd the cor-

responding binary trees from the tree-models. This

also makes the matching process faster and reduces

computation complexity which is higher with consid-

ering more levels of nodes in the binary trees. Once

a match is obtained, another binary tree is considered

from the respective tree-model to ﬁnd the correspond-

ing binary tree to the other tree-model. This matching

process continues until all the corresponding vessels

are obtained.

3 EXPERIMENTAL RESULTS

We match the tree-models obtained from the retinal

images of an individual. From this we trace the cor-

responding blood vessels to observe the changes in a

particular period of time. We note that each vessel

has a unique pattern on its feature vectors. Therefore,

we are always able to ﬁnd the corresponding vessel

or binary tree in the tree-models. Table 2 shows the

distance of different blood vessels in node number for

an image.

To observe the performance of our matching

method on the images taken at different conditions

(e.g., second image may be rotated or shifted while

capturing), we rotated the image from ±5

to ±30

AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING USING VASCULAR FEATURES

345

Table 3: The distance matrix with vessel 1 for different ro-

tating angles.

Distance with vessel1 in node number

Rotation Vessel1 Vessel2 Vessel3 Vessel4 Vessel5

0 7 7 7 7

2 7 7 7 7

4 7 7 7 7

Table 4: The distance matrix after applying the thresholding

with vessel 1 for different rotating angles.

Distance with vessel1 in node number

Rotation Vessel1 Vessel2 Vessel3 Vessel4 Vessel5

0 7 7 7 7

Unfortunately, we do not have the data on images

taken at different times for the same person. Hence,

to check the robustness of our method, we rotated the

same image and constructed the tree-model for each

rotated image. We then obtained the distance matrix

in node numbers for one vessel with the other ves-

sels. This is done by comparing the ﬁrst tree-model

which is obtained from the original image with the

tree-models constructed for the images in different ro-

tating angles. Table 3 shows the distance matrix for

vessel 1 with the original tree-model and the obtained

tree-model after rotating the image.

We observe that for smaller rotation angle, there is

no difference on the feature vectors’ attributes. With

higher rotation angle, some changes are introduced

in feature values such as vessel length, width and

crossover position. We observe that such errors are

introduced due to the discretization of the pixels’ ac-

tual position during rotation operation of the image.

To overcome this problem, we consider a threshold

value of ±5% for the length to width ratio and actual

crossover position and received the distance as 0 for

the same vessel while the distances with other vessels

remain the same. Table 4 shows the distance matrix

for vessel 1 after applying the thresholding technique.

Using the tree-models, we also compare the distances

between the same vessels in the images with this tech-

nique. Table 5 shows the distance matrix between the

same vessels in the image (from the two tree-models).

From these observations we can conclude that the

proposed vascular network model is suitable for com-

Table 5: The distance between the same vessel represented

by the two different tree-models after applying the thresh-

olding.

Distance between

Vessel 1 Vessel 2 Vessel 3 Vessel 4 Vessel 5

Rotation to to to 3 to to

Vessel1 Vessel2 Vessel3 Vessel4 Vessel5

0 0 0 0 0

paring vascular feature(s) between two images for pa-

tient longitudinal study. Our main focus is to ﬁnd

the corresponding vessel i.e., the binary tree from the

tree-models. In worst case, if the minimum number

of nodes are not matched from the corresponding bi-

nary trees, we can rely on the best match between

the binary trees assuming that we are matching the

tree-models from the same person’s images. Based

on this, we can decide about the corresponding bi-

nary trees. We evaluate the robustness of our method

for image matching and considered 20 images in the

STARE database. For each image the tree-model is

constructed and then the nodes are compared to ﬁnd

the distance matrix. We observe that each image is

unique for its vascular pattern. Table 6 shows the dis-

tance in node number between the tree-models for ten

different images.

4 CONCLUSIONS AND FUTURE

WORK

In this paper we described a novel approach for reti-

nal image matching which is highly suitable for pa-

tient longitudinal study. Initial results predicts that

the method will be very accurate in matching the reti-

nal images and ﬁnding the corresponding blood ves-

sels. Based on this method, the medical practitioners

can observe changes on different vascular features for

each of the vessel-segments. Simple modiﬁcation of

the method can also be suitable for biometric secu-

rity application. At present we are acquiring multiple

images for the same person to enable a large scale

study and further validation of the method. We aim

to perform two case studies in biometrics and patient

longitudinal study using our proposed retinal image

matching algorithm.

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346

Table 6: The distance matrix between the images.

The distance between the images in node number

One Two Three Four Five Six Seven Eight Nine Ten

One 0 31 42 34 47 29 56 48 49 51

Two 31 0 43 35 48 30 57 49 50 52

Three 42 43 0 46 59 41 68 60 61 63

Four 34 35 46 0 51 33 60 52 53 55

Five 47 48 49 51 0 46 73 65 66 68

Six 29 30 41 33 46 0 55 47 48 50

Seven 56 57 68 60 73 55 0 74 75 77

Eight 48 49 60 52 65 47 74 0 67 69

Nine 49 50 61 53 66 48 75 67 0 70

Ten 51 52 63 55 68 50 77 69 70 0

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