AN ADAPTIVE REGION GROWING SEGMENTATION FOR

BLOOD VESSEL DETECTION FROM RETINAL IMAGES

Md. Alauddin Bhuiyan, Baikunth Nath and Joselito Chua

Computer Science and Software Engineering, The University of Melbourne

Melbourne, Australia 3010

Keywords: Medical Image, Blood Vessel, Adaptive Region Growing technique, Gradient Operator, Segmentation.

Abstract: Blood vessel segmentation from the retinal images is extremely important for assessing retinal

abnormalities. A good amount of research has been reported on blood vessel segmentation, but significant

improvement is still a necessity particularly on minor vessel segmentation. As the local contrast of blood

vessels is unstable (intensity variation), especially in unhealthy retinal images, it becomes very complicated

to detect the vessels from the retinal images. In this paper, we propose an edge based vessel segmentation

technique to overcome the problem of large intensity variation between major and minor vessels. The edge

is detected by considering the adaptive value of gradient employing Region Growing Algorithm, from

where parallel edges are computed to select vessels. Our proposed method is efficient and performs well in

detecting blood vessels including minor vessels.

1 INTRODUCTION

Automatic detection of blood vessels in the retinal

images can help physicians with diagnosing ocular

diseases, patient screening, clinical study, etc. For

instance, a patient may exhibit discoloration of the

optic nerve, or a narrowing of the blood vessels in

the retina. An ophthalmologist (a medical doctor

specialized in the structure, function, and diseases of

the human eye) uses this information to diagnose the

patient, as having for instance Coats' disease or a

central retinal artery occlusion. A common

procedure to examine the eye health is passing

through the procedure of retinal imaging. An optical

camera (for instance, Mydriatic and non-mydriatic

retinal cameras) is used to see through the pupil of

the eye to the rear inner surface of the eyeball. A

picture is taken showing the optic nerve, fovea,

surrounding vessels, and the retinal layer. The

ophthalmologist can then reference this image while

considering any observed findings.

The most effective treatment for many eye

related diseases is the early detection through regular

screening. Assessment of the characteristics of

vessels in the retina plays an important role in

medical diagnoses. For these tasks measurements are

needed of e.g., vessel width, colour, reflectivity,

tortuosity, abnormal branching, or have the

occurrence of vessels of a certain width. Blood

vessel appearance can provide information on

pathological changes caused by some diseases

including diabetes, hypertension, and

arteriosclerosis. Changes in retinal vasculature, such

as haemorrhages, angiogenesis; increases in vessel

tortuosity, blockages and arteriolar-venular diameter

ratios are important indicators of, for example,

diabetic retinopathy, and retinopathy of prematurity

and cardiovascular risk. Information about blood

vessels in retinal images can be used in grading

disease severity or as part of the process of

automated diagnosis of diseases (Hoover et al.

2000).

The automatic detection of blood vessels is very

important as ophthalmologist can potentially screen

larger populations for vessel abnormalities. In

contrast, manual delineation of vessels becomes

tedious or even impossible when the number of

vessels in an image is large or when a large number

of images are acquired. Furthermore, a detection and

segmentation of the vascular tree seems to be the

most appropriate representation for the entire retinal

image due to three following reasons. Firstly, it

maps the whole retina. Secondly, it does not move

except in a few diseases. Finally, it contains enough

404

Alauddin Bhuiyan M., Nath B. and Chua J. (2007).

AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IFP/IA, pages 404-409

 SciTePress

information for the localization of some anchor

points (Chanwimaluang and Fan 2003).

Automated retinal segmentation is complicated

by the fact that the width of the retinal vessels can

vary from large to very small, and that the local

contrast of vessel is unstable, especially in unhealthy

retinal images (Li et al. 2006). Although a large

amount of research (Martinez-Perez et al. 1999;

Zana and Klein 1999; Hoover et al. 2000; Jiang and

Mojon 2001; and Li et al. 2006) has been published

for the detection of blood vessels, a huge

improvement in detection procedures remains a

necessity for the detection of small vessels

(branches).

In this paper we propose a novel vessel

segmentation technique based on vessel edges. The

proposed method employs the adaptive region

growing segmentation algorithm to overcome the

complexity of edge segmentation, as we cannot trace

them with a fixed intensity or gradient direction. The

gray values of the vessels in the retinal image are

changeable throughout the entire image and gradient

direction is not constant due to curvilinear structure

of the vessels. Based on these phenomena, it is

appropriate to apply the Adaptive Region Growing

(ARG) algorithm to detect the edges of vessels.

After segmenting the edges, we locate the parallel

edges based on gradient direction considering pixel

position of each region. These parallel edges are

considered as primary vessels and facilitate to

remove the noise and other objects. Finally, we map

the original retinal image based on pixel positions of

that segmented gradient image to detect the blood

vessels.

The rest of the paper is organized as follows:

section 2 presents a brief background and review of

the related segmentation based literature and section

3 discusses our proposed Adaptive Region Growing

segmentation based blood vessel detection method.

Section 4 provides preliminary results and

discussion. Finally, conclusions and future research

directions are drawn in section 5.

2 BACKGROUND LITERATURE

Previous methods for blood vessel detection in

images of the retina can be categorized into two

groups. The tracking based approach and Template

or Model based approach. Tracking based

approaches work by first locating an initial point and

then exploiting local image properties to trace the

vasculature recursively. This technique may require

user intervention and appear to have proclivity for

termination near branch points. Model based

approaches apply explicit vessel models to extract

the vasculature.

Chaudhuri et al. (Chaudhuri et al. 1989)

introduced an algorithm based on directional two-

dimensional (2-D) matched filters to detect

piecewise linear segments of blood vessels. Twelve

different templates were used to search for vessel

segments along all possible directions. This method

is completely unsupervised and good for initial

estimation. However, the detected vessels may not

be continuous and small vessels get missed and

validity of the detected vessels is not checked.

Tolias and Panas (Tolias and Panas 1998)

presented a fuzzy vessel tracking algorithm for

retinal images based on fuzzy clustering. Salient

regions are initialized as starting point for vessel

tracking. Then Fuzzy C-means clustering algorithm

determines vessel and non-vessel region along a

vessel profile. Then trace the vessel based on

thresholding the membership values. This is also an

unsupervised technique and performed well on

detecting major vessels (98%). Nonetheless, this

algorithm still suffers from missing a large number

of minor vessels (detection rate only 23.53%).

Mendonca and Campilho (Mendonca and

Campilho 2006) proposed an algorithm based on

region growing process using vessel centreline as

seed point. Multi-scale morphological vessel

enhancement applying a modified top-hat transform

with variable size structuring elements is performed.

In order to obtain binary maps of the vessels a

binary morphological reconstruction is used. A set

of four directional differences of offset Gaussian

filters is used to detect vessel centreline. Vessel

filling by region growing process using as initial

seeds the pixel within the centrelines is used. The

growing is successively applied to the four scales

and, in each region growing step; the seed image is

the result of the previous aggregation. This

technique shows an improved detection rate with

accuracy of 94.7%.

Staal et al. (Staal et al. 2004) presented a ridge

based vessel segmentation algorithm in color retinal

images. The ridge is detected by applying Gaussian

scale space technique and grouped by applying

region growing algorithm. Therefore, the image is

grouped into patches or convex sets. Features of the

convex sets and the pixels belonging o the convex

sets are considered to construct vectors and

classified by the KNN (k-nearest neighbour)

classifier. Convex sets’ features are height, width

and their ratio, curvature, distance between first and

last points of a convex set, mean and standard

AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL

IMAGES

405

deviation of a green patch, etc. Pixel features are

value of red and green plane of the image at the

pixel location and their ratio, etc. This technique

also shows promising detection rate with maximum

accuracy of 0.944.

Wu et al. (Wu et al. 2006) introduced an adaptive

detection of blood vessels in the retinal images. At

first the blood vessel enhancement is performed by

adaptive histogram equalization technique. Then

vessels features are extracted using the standard

deviation of Gabor filter responses along different

orientations. Finally, the vessel is traced using

forward detection, backward verification and

bifurcation detection. The overall detection rate is

80.15% while small vessel pixel detection rate 42%

and small vessel detection rate 75%.

Jiang and Mojon (Jiang and Mojon 2003)

presented an adaptive local thresholding technique

by verification-based multithreshold probing to

detect blood vessels in the retinal images. At first,

the original retinal image is converted into binary

image through multiple thresholding by considering

curvilinear structure and width of the vessels. Then

Euclidian distance transformation from candidate

vessel point to background point is performed.

Following that the vessel candidate is pruned by

means of the distance map to only retain centreline

pixels (considering distance of two nearest

background pixel & angle from these two points) of

curvilinear bands. Finally, the curvilinear bands are

reconstructed from their centreline pixels. The

reconstructed curvilinear bands give the part of the

vessel network that is made visible by the particular

threshold. The overall detection rate reported is

86.5%. This technique needs further improvement in

vessel detection and background noise suppression.

Zana and Klein (Zana and Klein 2001) presented

a vessel segmentation algorithm using mathematical

morphology and curvature evaluation. At first the

vessels are highlighted using their morphological

properties (sum of top hats reduces small bright

noise and improve the contrast of all linear part).

After that the cross curvature is evaluated using the

Laplacian operator. Then the alternating filter is

used to produce the final result. The technique is not

sensitive to sudden changes in the global gray level.

However, results in missing pixels of the dilated line

because of surrounding texture.

Hoover et al. (Hoover et al. 2000) proposed an

algorithm for locating blood vessels in retinal

images by piece-wise threshold probing of a

Matched Filter Response (MFR). At first, the

original image is filtered by MFR. Then the filtered

image is thresholded and thinned. Finally, use the

probing technique while the probe examines the

image in pieces (initial threshold is the MFR image

value at the starting pixel, then regions grow using a

conditional paint-fill technique), testing a number of

region based properties (e.g., segment length). If the

probe decides a piece is vessel (if the resulting

region belong to a minimum number of threshold

pixels but less than maximum or connects two

previously probed pieces, then the region is labelled

as vessel), then the constituent pixels are

simultaneously segmented and classified. The

overall detection rate is 90% true positive and 4%

false positive. This technique has limitations on

detecting background or non vessel removal.

3 PROPOSED METHOD

As we mentioned earlier that the automated retinal

segmentation is complicated by the fact that the

local contrast of vessels is unstable, the width of

retinal vessels can vary from very large to very small

especially unhealthy ocular fundus images. We

present a method for the segmentation of blood

vessels in retinal images based on the partial

derivative of intensity image, which gives

information about its topology and also overcome

the problem of image intensity variation. We use

STARE (Hoover 2002) retinal imaging dataset. Our

proposed method performed much better in

detecting both major and minor vessels.

The procedure is as follows: at first we enhance

the contrast of the original retinal image by applying

Adaptive Histogram Equalization method then we

apply the first order directional derivative operator,

normalize it and convert the original image into

gradient image. Each vessel will show up as parallel

edges, which will be segmented by applying

Adaptive region growing algorithm. Since the

contrast of the vessels is unstable it is not viable to

apply a threshold value to segment the edges of a

vessel. Due to the curvilinear structure of the vessels

the gradient direction is also changeable. So, we

apply Adaptive value of gradient magnitude with

region growing process to segment the edges.

Parallel edges are selected considering the gradient

direction of each pixel belonging to the parallel

regions. We can, therefore, segment the vessels and

remove the background noise and other objects.

Finally, we map the vessel pixels from the original

retinal image based on the segmented gradient image

to show the detected vessels.

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

406

Figure 1 displays the overall technique of our

proposed method. We describe each step in detail in

the following subsections.

Figure 1: The vessel segmentation model.

3.1 Preprocessing of Retinal Image

In the preprocessing step the aim is to enhance the

contrast of the original retinal image. The Adaptive

Histogram Equalization method is implemented,

using MATLAB, to enhance the contrast of the

image intensity by transforming the values using

contrast-limited adaptive histogram equalization. It

operates on small regions in the image, called tiles,

rather than on the entire image. Each tile contrast is

enhanced, so that the histogram of the output region

approximately matches the histogram specified by

the 'Distribution' parameter. The neighboring tiles

are then combined using bilinear interpolation to

eliminate artificially induced boundaries.

3.2 Image Conversion

The enhanced retinal image is converted into

gradient image using first order partial differential

operator. The gradient of an image f(x,y) at location

(x,y) is defined as the two dimensional vector

(Gonzalez and Wintz 1987)

G [f(x,y)]= [G

] =

⎥

⎦

⎤

⎢

⎣

⎡

∂

(1)

It is well known from vector analysis that the

vector G points in the direction of maximum rate of

change of f at location (x,y). For edge detection, we

are interested in the magnitude of the vector,

generally referred to simply as the gradient and

denoted G [f(x,y)] and commonly takes the value of

G [f(x,y)]

≈ |G

| +

|. (2)

The direction of the gradient vector is calculated

as follows. Letting

),( yx

represent the direction

angle of G at location (x,y),

),( yx

=tan

-1

/ G

) (3)

where the angle is measured with respect to the x-

axis.

Figure 2: Original retinal green channel image (left) and

its Adaptive Histogram Equalized Image (right).

Figure 3: Gradient image of the Adaptive histogram

equalized of Figure 2.

3.3 Adaptive Region Growing

Technique

The edges of vessels are segmented using region

growing procedure that groups pixels or sub regions

into larger regions based on gradient magnitude. As

the gradient magnitude is not constant for the whole

vessel we need to consider an adaptive gradient

value that gradually increases or decreases to append

the pixel to a region. We call it an adaptive

procedure, as the difference of neighbouring pixels

intensity value is always adapted for the region

growing process.

The region growing process starts with

appending the pixels that pass certain threshold

value (Gonzalez, Woods et al. 2004). For region

growing we find the intensity difference between a

pixel belonging to a region and its neighbouring

potential region growing pixels. The pixel is

considered for appending in that region if the

difference is less than a threshold value. The

threshold value is calculated by considering the

maximum differential gradient magnitude for any

neighbouring pixels with equal (approximately)

AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL

IMAGES

407

gradient direction. Region growing should stop

when no more pixels satisfy the criteria for inclusion

in that region. In the region growing process each

region is labelled with a unique number. For that

purpose we construct a cell array with region

number and its pixel position. The image is scanned

in a row-wise manner until its end, and each pixel

that satisfies our criteria is taken into account for

growing a region with its 8-neighborhood

connectivity.

Figure 4: Output image after applying adaptive region

growing with minimum pixel number.

3.4 Parallel Region Detection

We calculate the parallel edges (regions) by

considering pixel orientation belonging to each

region. At first, we pick the region number and

belonging pixel coordinates from the constructed

cell array. Then we grouped the region/regions

parallel to each region, which is calculated by

mapping the pixels gradient direction. For each

region every pixel is searched from its potential

parallel region and once a maximum number of

pixels match with the other region we consider it as

parallel to that region. We consider all regions and

once a region is considered we assigned a flag value

to that region so that it will not be considered again.

In this way we can only filter the vessels from the

region and discard all other regions, which are

background noise or other objects like haemorrhage,

macula, etc in the retinal image.

3.5 Vessel Detection

We mapped the original retinal image with the

pixels from the segmented gradient image to show

the vessels. We can also find the centreline of each

vessel (parallel region pixels) edges and then expand

it with setting the stopping criteria of facing edge

pixels to determine the total pixels of each vessel.

For simplicity, we only produce the images with

mapping the original retinal images. Figure 5 below

displays two examples of images from the dataset

and their output.

Figure 5: Original retinal image (left) and detected vessels

(right).

4 RESULTS AND DISCUSSION

Figure 5 shows two example images and the output

after blood vessel segmentation using the proposed

method. The first retinal image is almost a normal

eye and we observe that 99% vessels are detected

properly. However, the second retinal image suffers

from noise and the detection rate falls to

approximately 90%. This fall in detection rate is due

to the fact that if any part of the retinal image suffers

from noise like block (eg. haemorrhage) then the

whole area is removed as we consider only the

parallel regions. Therefore, it is possible to miss

vessels from that particular part of the retinal image.

Table 1 displays observations of vessel detection for

five different images.

5 CONCLUSION AND FUTURE

WORK

In this paper we proposed a novel approach for

adaptive region growing technique and applied to

retinal gradient images. The results obtained are

promising. Currently, we are working on to produce

the retinal output binary image by expanding the

vessel centreline, which can be used as a vessel

width and crossover measurement.

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

408

Table 1: Vessel detection accuracy.

Image

Total

Number of

vessels

Number of

detected

vessels

Accuracy

Image 1 93 92 98.92

Image 2 74 70 94.59

Image 3 85 79 92.94

Image 4 81 76 93.82

Image 5 75 71 94.66

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