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2011 Eighth International Joint Conference on Computer Science
and Software Engineering (JCSSE)
Automatic Extraction of Retinal Vessels Based on
Gradient Orientation Analysis
Danu onkaew1, Rashmi Turior2, Bunyarit Uyyanonvara3, Toshiaki
Kondo4
1,2,3,4 Sirindhorn International Institute of Technology,
Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang,
Pathumthani 12120
Thailand Ipingpong---'[email protected],
[email protected], 3bunyarit@siit. tu.ac.th,
4tkondo@siit. tu.ac.th,
Abstract-Retinal vessel extraction is important for the
diagnosis of numerous eye diseases. It plays an important role in
automatic retinal disease screening systems. This paper presents an
efficient method for the automated analysis of retinal images.
Fine
anatomical features, such as blood vessels, are detected by
analyzing the gradient orientation of the retinal images. The
method is independent of image intensity and gradient magnitude;
therefore, it performs accurately despite the common problems
inherent to the retinal images, such as low contrast and
non-uniform illumination. Blood vessels with varying diameters are
detected by applying this method at multiple scales. The blood
vessel network is then extracted from the detected features
by manual thresholding followed by a few simple morphological
operations. Based on the binary vessel map obtained, we attempt to
evaluate the performance of the proposed algorithm on two
publicly available databases (DRIVE and STARE database) of
manually labeled images. The receiver operating characteristics
(ROC), area under ROC and segmentation accuracy is taken as the
performance criteria. The results demonstrate that the proposed
method outperforms other unsupervised methods in
respect of maximum average accuracy (MAA). The proposed method
results in the area under ROC and the accuracy of 0.9037, 0.9358
for DRIVE database 0.9117, 0.9423 for STARE database
respectively.
Keywords-component; retinal image; feature detection; feature
extraction; blood vessels; gradient orientation
I. INTRODUCTION
Retinal images are widely used in the diagnosis and treatment of
various eye diseases and also systemic diseases, such as diabetes,
hypertension and retinopathy of prematurity. An automated method
for analyzing retinal images is particularly important to deal with
mass screening of the images [1], [2].
The automated analysis of retinal images is a difficult task
because the images taken at standard examinations are often noisy
and poorly contrasted. Besides that, there are intensity variations
across the image caused by non-uniform illumination of the retina,
and also intensity variations between images. Much effort has been
made to cope with these difficulties [1]-[9]. Unlike the previous
work, however, this paper presents a novel method that is free from
these common problems. The method does not directly depend on the
image
intensity and gradient magnitude, but is based on gradient
orientation. For this, the method can detect vital anatomical
features in retinal images robustly without requiring any
preprocessing for image enhancement and the compensation of
irregularities of illumination.
This paper is organized in four sections. In section II, a
schematic overview of our methodology is demonstrated and explains
step by step techniques required for retinal blood vessel
segmentation. Experimental results and evaluation of the algorithm
on the images of the DRIVE [9] and STARE [7] database are given in
section III followed by Discussion. Finally, conclusion and future
work is given in section IV.
II. METHODOLOGY
A. Gradient Orientation Analysis
This method aims to obtain the gradient vectors of the image and
normalize them into the unit gradient vectors, as we are only
interested in gradient orientation. The unit vectors converge on
(or diverge from) anatomical features as long as the features are
brighter (or darker) than background. Therefore, the unit vectors
are highly discontinuous where there are features especially with
radially and bilaterally symmetrical structures. Radially
symmetrical structures include circular features, such as micro
aneurysms and hemorrhages (dark spots); cotton wool spots and
exudates (white spots), while bilaterally symmetrical structures
correspond to linear features represented by retinal blood vessels.
We detect these features in retinal images by finding
discontinuities in gradient orientation. It is emphasized that this
technique is effective irrespective of image intensity and
contrast, because the magnitude of the gradient vectors is
�t.£�s9> [8].
The procedure of gradient orientation analysis (GOA) is
depicted, referring to Fig. 1. Let denote a retinal
image (Fig. 1 (a)). Fig. l(b) shows an enlarged sub image that
corresponds to the small region encompassed with the black square
box in Fig. 1 (a). The sub image contaIA�,W'!owcontrasted blood
vessel together with bright speckles that
indicate some pathology. The gradient vectors of are
approximated by partial derivatives:
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{gx(X,Y) : g(X,Y) : kx gy(x,y) - g(x,y) ky (1)
where * denotes convolution, and kx and ky are firstderivative
operators in the x (horizontal) and y (vertical) directions,
respectively. As shown in Fig. ( c), prominent gradient vectors are
seen around the boundaries of the white speckles as they are highly
contrasted, while the gradient vectors corresponding to the blood
vessel are not visible because of their low contrast. Fig. led)
shows the unit gradient vectors obtained by
{nx (x,y) = gx(x,y)/ Jgx 2 (X ,y) + g/(x,y) (2)
ny (x, y) = gy(x,y)/ Jgx \x,y) + gy \x,y)
where we assign O's to the unit vectors (nx (x, y), ny (x,
y))
if the denominator Jgx\x,y)+ g/(x,y) is too small(:S;20). By
discarding the gradient magnitude, we can now locate the blood
vessel easily in Fig. 1 (d). To find discontinuities in gradient
orientation, we compute the fIrst derivatives of the unit
vectors:
(3)
Where the same fIrst-derivative operators, kx and ky , are once
again used. The discontinuity magnitude of gradient
orientation D(x, y) may be expressed as
D\x, y) = d= 2 (x, y) + d '9' \x, y) + dy}(x,y) + dy/(x,y)
(4)
It is interesting to mention that D2 (x, y) takes smaller values
for random patterns because of the smoothing effect of the
derivative operator. D2 (x, y) is obtained by summing the fIrst
derivative of unit vectors in all directions. It should be noted
that GOA responds strongly to discontinuous but highly structured
patterns. This feature is especially useful for the detection of
linear and circular structures.
Figure I Concept of gradient orientation analysis. (a) A retinal
image. (b) An enlarged subimage corresponding to the small region
encompassed with the black square shown in (a). ( c) Gradient
vectors of the subimage. (d) Gradient orientations of the subimage
indicated by the unit gradient vectors.
B. Multi-Scale Approach
It is essential to employ proper fIrst-derivative operators as
they determine the size of features that the method can detect.
This paper mainly focuses on the detection and extraction of the
vascular net (the network of blood vessel). The width of the vessel
varies widely as it travels radially from the optic diskl. With the
aim of detecting various sizes of features, we apply GOA at three
different scales. The Sobel operator is fIrst used as k x and k
y to detect very fIne features in an original image
(Fig. 3 (a)). To detect larger features, we modify the Sobel
operator as
1 0 0 0
o 0 0 0
k� = 2 0 0 0 o 0 0 0
1 0 0 0
-1
0
-2
0
-1
k' = , y
1
0
0
0
-1
0 2
0 0
0 0
0 0
0 -2
0
0
0
0
0
o
o
o
-1
and use this to the original image (Fig. 3(b)) and also a
quartersized sub image (Fig. 3(c)). Denoting the discontinuity
magnitude of gradient orientation at each scale Dj (x, y) , D2 (x,
y) , and D3 (x, y) , respectively, we defme a response of GOA,
DGoix,y), as
where, D3 (x, y) is resized to the original image size by
up-sampling (Fig. 3(d)).
1 The optic disk is the entrance of blood vessels and optic
nerves from the brain to the retina.
103
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Figure 2 D2 maps at three scales for an image from STARE
database .. (a) Dj 2 map obtained by using the Sobel filter as a
derivative operator in an original image. (b) Di map obtained by
using a modified Sobel filter in an original image. (c) D; map
obtained by using a modified Sobel filter in a quarter-sized
subimage. (d) G2 map obtained by integrating the three D2 maps.
GOA
C. Extraction of Blood Vessels
It is important to note that GOA responds to valleys and
ridge-like structures, as they are discontinuous in gradient
orientation. To extract blood vessels (i.e. , valleys), high GOA
responses are estimated.
We then apply thresholding to D�alley (x, y) to obtain a binary
map, in which the threshold value is selected such that
the largest of D�alley(X,y) are extracted. In our case a
threshold value of 0.21 was used for Drive database and 0.09
threshold for Stare Database.
Subsequently, we apply two simple mathematical morphology
operations. A closing operation is first applied, which performs
dilation followed by erosion. Next, a filling operation is applied
to fill isolated interior pixels O's surrounded by l's. The
structuring element used for the morphological operations above is
a 3x3 matrix containing only l's.
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III. RESULTS AND DISCUSSIONS
The performance of the proposed method is evaluated using two
publicly available databases of retinal images and manual
segmentations: the DRIVE and STARE. We have tested the method on
the twenty retinal images from Stare
database of size 605x700 with 24 bits per pixel (standard RGB)
used in [7]. Ten of the images are of patients with no pathology
(normals), while the other ten images contain pathology
(abnormals). There are 40 color images in DRIVE database [9]. The
size of each image is 565x584 pixels. The set of 40 images is
divided into training and a test set and each set consist of 20
images. There are a single and two manual segmentations available
in training set and test set respectively. All human observers were
instructed and trained by ophthalmologist in order to provided a
manual segmentation. Since the green band of the RGB data
contains
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Figure 3 D2 maps at three scales for an image from DRIVE
database. (a) D,2 map obtained by using the Sobel filter as a
derivative operator in an original image. (b) D; map obtained by
using a modified Sobel filter in an original image. (c) D; map
obtained by using a modified Sobel filter in a quarter-sized
subimage. (d) G�OA map obtained by integrating the three D2
maps.
the most useful information, we apply GOA only to the green band
(see Fig. 4 for two examples). The responses of GOA,
D�OA(X, y), are scaled in 256 gray levels where lower gray
levels indicate greater GOA responses. It is apparent that the
method detects features regardless of the low contrast and the
intensity variations across the images. Fig. 6 shows
D�alley (x,y) maps, also scaled in 256 gray levels, which
illustrate features with valley structures, i.e. the vascular
net.
Since D�OA(X, y) and D�alley (x, y) serve as a robust feature
map, it can be used not only for diagnosis but also for
applications, such as image registration and personal
identification. Blood vessels are then extracted from
D�alley (x,y) by manual thresholding followed by the
morphological operations described above (Fig. 7). Based on the
ground truth data in [7], the true positive rate (sensitivity),
that is, correctly detected blood vessels over true blood
vessels, is 87.74% and specificity obtained is 82.67%, on average
for Stare Database evaluated by optimizing the threshold values on
binary images as shown in Table 1. The results are comparable with
or slightly better than those of previously published [4], [6], [7]
and [8].
Finally, we used three performance measures to evaluate the
performance of the algorithm. The first is receiver operating curve
(ROC). An ROC space is defined by false Positive rate (FPR) and
true positive rate (TPR) as x and y axes respectively, which
depicts relative trade-offs between true positive (benefits) and
false positive (costs). Since TPR is equivalent with sensitivity
(SN) and FPR is equal to (1 -specificity), the ROC graph is
sometimes called the sensitivity vs (1 - specificity) plot. The SN
and SP is obtained as follows:
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Both measures are evaluated using the four metric values-true
positive (TP), sum of pixel marked as vessel in both result
Figure 4 Left: An example image from the DRIVE database
containing pathology. Right: An example image from the STARE
database without pathology.
Figure 5. Dialley (X, y) maps that present features with valley
structures in the normal retinal image from Stare (down) and the
abnormal retinal image (up)from Drive database.
and ground truth image; false posItIve (FP), sum of pixel marked
as a vessel in result image but not in ground truth image; false
negative (FN), sum of pixel marked as a background in result image
but not in ground truth image; true negative (TN), sum of pixel
marked as a background in both result and ground truth image. The
sensitivity and the specificity are computed as shown in equation
(6) and (7) respectively. We create the ROC curve by varying the
threshold on the soft classification image as shown in Fig.
(9),(10). It is observed that the ROC shows better performance for
the proposed GOA method as compared to our previous result using
Combination of Bottom Hat Transform and Matched filters used in
[10].
The second is the area under ROC (Az). the larger are under the
curve (Az) signified a greater discriminatory ability of the
segmentation method. The third measure is maximum average accuracy
(MAA). The accuracy of an image is calculated by taking the sum for
the TP and TN dividing by sum of the total number of vessel pixels
(P) and total number of non vessels (N) as illustrated in equation
(8). In our experiments, we used the manual segmentation by 1 st
observer of DRIVE database and segmentation that provided by Hoover
of STARE database as a gold standard for calculating all these
three measures- ROC, area under ROC, and MAA, only pixels inside
the field of view (FOV) is taken into account.
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Figure 6 Blood vessel maps showing extracted blood vessels in
the normal retinal image (down) and the abnormal retinal image
(up).
Fignre 7 Ground truth image of normal retinal image from Stare
(down) and the abnormal retinal image (up )from Drive database.
True blood vessels True background Extracted as blood vessels
True positive (TP) False positive (FP) Extracted as background
False negative (FN) True negative (TN)
TP Sensitivity = True positive rate (TPR) = ----
TP+FN
TN Specificity = True negative rate (TNR) = ----
TN+FP
TN+TP Accuracy = ----
P+N
Table 1: Accuracy of Extracting blood vessels.
True Positive Rate (Sensitivity) 87.74%
True Negative Rate (Specificity) 82.67%
IV. CONCLUSION AND FUTURE WORK
(6)
(7)
(8)
This paper presents a robust and efficient method for detecting
blood vessels in retinal images despite the inherent problems of
the images, such as low contrast and intensity variations. The
method, solely based on the analysis of gradient orientation, is
not directly affected by image intensity. Therefore, no
preprocessing for image enhancement and illumination equalization
is required. Since features are detected by finding high
discontinuities in gradient orientation, the method works as a
robust crease-edge detector, which is
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well suited for detecting linear and circular structures. No
Figure 8 The proposed method ROC curve for the STARE
database
techniques [4] and locally adaptive methods [6], are employed.
ill addition, unlike matched-filter based methods [7], numerous
large convolution masks are not necessary. Coupled with a
multi-scale approach, features with various sizes and orientations
can be detected by the Sobel filter (3x3) and its extension (5x5),
which makes the method computationally highly efficient.
As future work, we plan to detect patches or blobs in abnormal
images and make the fixed parameters used in the method adaptive to
improve performance. We will then work on further analysis of the
extracted blood vessel network to detect abnormality in the infant
retinal images using a larger database.
ACKNOWLEDGMENT
This research is financially supported by Thailand Advanced
illstitute of Science and Technology (TAIST), National Science and
Technology Development Agency (NSTDA) Tokyo Institute of Technology
and Sirindhom illternational illstitute of Technology (SIlT),
Thammasat University (TU).
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