Journal of Medical and Biological Engineering, 31(6): 405-412 405

Detection of the Optic Disc on Retinal Fluorescein Angiograms

Shangping Liu Ji Chen*

College of Bioengineering, Chongqing University, Chongqing 400044, China

Received 1 Apr 2010; Accepted 7 Sep 2010; doi: 10.5405/jmbe.773

Abstract

A fast and efficient approach for localizing and segmenting the optic disc in fluorescein retinal images by means of

mathematical morphology and the gradient vector flow (GVF) snake model is proposed. The localization algorithm

utilizes the similarity in gray intensity between blood vessels and the optic disc. In the approach, the retinal images are

first enhanced using two top-hat operators, where the optic disc is localized using Otsu’s threshold and image

subtraction. After localization, pixel-level preprocessing is performed to remove blood vessels, where the optic disc

boundary is detected using the GVF snake model. The proposed method is evaluated using a database of 60 fluorescein

images. The accuracy rate of disc localization is 96.7%. The boundary detection method has average sensitivity values

of 95.1% for images with a defined optic disc and 90.4% for images without a defined optic disc.

Keywords: Optic disc, Retinal images, Mathematical morphology, Gradient vector flow (GVF) snake model, Boundary

detection

1. Introduction

Optic disc examination is one of the most important

diagnostic procedures in ophthalmology. The shape, area,

color, and depth of the optic disc are indicators used to measure

the health status of the human retina. Periodic observation of

the optic disc is used to monitor the evolution of eyeball’s

diseases. Localizing and segmenting the optic disc in retinal

images in order to find changes is useful for the diagnosis of

eye diseases such as optic atrophy, optic neuritis, papilledema,

optic neuropathy, glaucoma, diabetes, and some systemic

diseases of the human body [1,2]. The localization and

segmentation of the optic disc is an important step in macula

and exudate detection, vessel tracking, retinal image

registration and mosaic, and other automatic retinal image

analysis.

An optic disc in a typical color retinal image usually

appears as a bright yellowish circular object, which is the

region of convergence for the blood vessel network. However,

certain diseases may affect the appearance of the optic disc.

The optic disc is generally brighter than the surrounding area

with a clearly discernible elliptical contour. The distinction

between color retinal images and fluorescein retinal images is

shown in Fig. 1. In gray-level version of a color retinal image

such as that in Fig. 1(b), the gray intensity of the optic disc is

higher than that of the blood vessels and background. In a

fluorescein retinal image such as that in Fig. 1(c), the gray

* Corresponding author: Ji Chen

Tel: +86-23-65104496; Fax: +86-23-65111931

E-mail: chenji@cqu.edu.cn

(a) (b)

(c)

Figure 1. Difference between a fluorescein retinal image and a color

retinal image. (a) Color retinal image. (b) Gray-level version of

the color image in (a). (c) Fluorescein retinal image.

intensity of the optic disc is slightly lower than that of the

blood vessels but higher than that of the background. The optic

disc is defined as all areas inside the peripapillary scleral ring,

which amounts to approximately one seventh of the whole

retina region. Methods for optic disc segmentation include two

steps, namely optic disc localization and disc boundary

detection.

J. Med. Biol. Eng., Vol. 31 No. 6 2011 406

Various methods and algorithms have been developed to

localize the optic disc. They can be divided into four categories,

namely those based on (1) brightness, highest intensity, or

maximum variance [3-6], (2) the circular Hough transform

[7-10], (3) matched filtering or template matching [11-13], and

(4) the convergence of blood vessels [8,14-16].

Optic disc boundary detection is usually performed after

identifying the approximate position of the disc. The boundary

detection approaches can be divided into three main groups:

those based on morphological filtering and the active contour

model [13,16-18], those based on the Hough transform [19-21],

and those based on the active shape model [5,22]. Topological

Active Nets integrated region and edge features have been

proposed to find the precise contour of the optic disc [23].

Carmona et al. [24] first obtained a set of hypothesis points

which exhibited geometric properties and intensity levels similar

to those of optic disc contour pixels, and then used a genetic

algorithm (GA) to find an ellipse containing the maximum

number of hypothesis points.

For fluorescein retinal images, the following three factors

must be considered. First, fluorescein retinal images

significantly differ from color retinal images (Fig. 1). It is

difficult to identify the optic disc using its relatively higher

brightness. Second, retinal images may contain pathological

areas (Figs. 2(a) and (b)), show a partial optic disc (Fig. 2(c)) or

have uneven illumination (Fig. 2(d)).

(a) (b)

(c) (d)

Figure 2. Challenges faced by optic disc localization algorithms. Images

with (a) small pathological areas, (b) large pathological areas,

(c) partial optic disc, and (d) uneven illumination.

In Fig. 2(a), the optic disc of small pathological areas

cannot be identified based on the brightness. Similarly, the optic

disc of large pathological areas cannot be identified from the

shape, brightness, and convergence. For partial optical disc, the

optic disc cannot be identified based on the shape. The optic

disc of an uneven illumination requires more information than

the shape, brightness, size, and convergence. Thirdly, algorithms

proposed to segment the optic disc from the color retinal images

are inapplicable to the fluorescein retinal images. Based on the

gray intensity distribution of the fluorescein retinal images, this

paper proposes a novel and simple localization and

segmentation method that utilizes the similarity in gray intensity

between the optic disc and its inner vessels. First, the position of

the optic disc is found using morphological filters and Otsu’s

threshold method. Then, pixel-level image preprocessing

techniques are performed to remove blood vessels and noise.

Finally, the optic disc is segmented using the gradient vector

flow (GVF) snake model.

2. Methods

2.1 Optic disc localization

Because the localization algorithm is based on the

similarity in gray intensity between blood vessels and the optic

disc, a series of experiments was carried out on 10 fluorescein

images to confirm the similarity. In the first step, the images

were sent to experienced ophthalmologists, who manually

marked the disc boundary and its inner vessels. Then, the

average gray intensity of each optic disc and its inner vessels

were calculated, respectively.

Original fluorescein

image

Top-hat transform with

large circular

structuring element

Top-hat transform with

small circular

structuring element

Otsu’s threshold

method

Otsu’s threshold

method

Subtracion

Removal of small areas

Determination of the

rough location

Figure 3. Flow chart of proposed localization algorithm.

A flow chart of the proposed localization algorithm is

shown in Fig. 3. The top-hat operator is an excellent high-pass

filter calculated by subtracting the opened image from the

original image:

( )h f f b (1)

where ( ) denotes the opening operation for gray-scale

images, f is the input image, and b is the structuring element of

opening. The method is effective for extracting dark pixels

from bright backgrounds and extracting bright pixels from dark

backgrounds. The shape and size of the structuring element is

determined by the interested information from the input image.

Considering that the blood vessels in the image need to be

extracted, a circular structuring element is chosen.

Retinal Optic Disc Detection 407

The size of the structuring element is important. For a

small circular structuring element, the outer vessels of the optic

disc should be enhanced but the inner vessels should not; the

radius r of a small circular structuring element should thus

approach half of maximum width of the blood vessels in the

retinal image. For a large circular structuring element, both the

inner and outer vessels should be enhanced; the radius R of a

large circular structuring element should thus be larger than r.

In the present study, the radius R is assigned to be twice as

large as radius r.

After the two top-hat operators have been performed, the

gray intensity difference between the two enhanced images is

significant in the disc region. Otsu’s threshold method is applied

to the two enhanced images. Otsu’s method [25] is based on the

gray histogram and the maximum between-class distance. When

the between-class variance reaches the maximum value, the

optimal threshold is found and the algorithm achieves its best

segmentation results. Then, the optic disc region is easily

extracted using by subtracting the two binary images. After

subtracting, there are a few tiny vessels, burrs of vessel edges

and ends, and background noise. Compared to the main vessels

in the disc region, these areas are much smaller and therefore,

they can be easily removed by morphological processing.

Suppose that there are N white pixels in the image resulted from

subtracting and morphological processing and (xi, yi) represents

an arbitrary white pixel in the image after morphological

processing. The approximate position (x0, y0) of the optic disc

can be calculated using:

0 0

1 1

( , ) ( , )N N

i i

i i

x y x N y N

(2)

2.2 Image pre-processing

According to the approximate position (x0, y0) of the optic

disc, the candidate region (x0 − m, x0 + m; y0 − m, y0 + m) of the

optic disc can be obtained. The m value varies with the size of

the retinal image. For fluorescein retinal images, since the gray

intensity difference between the optic disc and its inner blood

vessels is not obvious, parts of the disc boundary may not be

well defined, and certain parts are partly obscured by the blood

vessels, which makes it difficult to use the boundary detection

algorithm. It is thus necessary to preprocess the images and

make them more suitable for boundary detection.

2.2.1 Removing blood vessels

Since the blood vessels obscure parts of the optic disc

contour, they need to be removed. Generally, blood vessels can

be removed from the original color retinal image using

mathematical morphology. However, due to the gray intensity

distribution of fluorescein retinal images, the use of

mathematical morphology is not suitable for low-contrast

fluorescein images. In the present study, pixels corresponding

to vascular structures are replaced with their nearest neighbor

pixels representing the background. The vascular structures are

extracted using adaptive histogram equalization and

morphological reconstruction. The adaptive histogram

equalization developed by Wu et al. [26] is used to enhance

blood vessels. To apply the adaptive histogram equalization to

an intensity image I, each pixel p in image I is adapted using

the following equation:

2

( )( ) ( ( ) ( )) /

t

AHE p R pI p s I p I p h M

(3)

where IAHE is the equalized image, M = 255, R(p) denotes the

pixel p’s neighborhood (a square window with length h), I(p')

denotes the gray intensity of a pixel p' in R(p), and s(d) = 1 if

d > 0 and s(d) = 0 otherwise. The parameter t is assigned as 2

or 6 and is scale-invariant.

The two images that result from the adaptive histogram

equalization are used for reconstructing potential vessels.

Reconstruction is a morphological transformation involving

two images. One image, the marker, is the starting point for the

transformation. The other image, the mask, constrains the

transformation. Otsu’s threshold method is applied to obtain the

mask and marker images.

After morphological reconstruction, morphological

dilation is applied to determine the connected components.

Assume that I1 is the original image of the optic disc region and

I2 is the binary image after thresholding and morphological

filtering. For each pixel (x, y) in image I2, if its gray intensity

I2(x, y) is equal to 1, its nearest neighbor pixel (m, n) with zero

gray intensity is found using a distance transform. Then, in

image I1, the intensity of pixel (x, y) is replaced by the intensity

of its corresponding pixel (m, n).

2.2.2 Image smoothing

After the blood vessels have been removed, the

illumination of the image is uneven and the contour of the optic

disc is not smooth. In order to remove noise and smooth the

contour, an average filter is applied to the image. Each pixel

(i, j) in input image I is adjusted as follows:

( , ) ( , ) ( , )WsmoothI i j I i j k I i j (4)

where Ismooth is the smoothed image and I—

W (i, j) is the mean

intensity of the pixels within a window W of size N × N. The

constant k is selected to be between 0 and 1.

2.3 Boundary detection using GVF snake model

Once the position of the optic disc is identified and the

candidate region of the optic disc is obtained and preprocessed,

the optic disc boundary detection is performed using the GVF

snake model.

Traditional snakes, which match a deformable model to an

image, are extensively used in image segmentation. There are

two major disadvantages associated with traditional snakes.

First, the initialization of the snake must be sufficiently close to

the desired contour in the image for the snake to evolve

correctly towards the desired contour. Secondly, it is difficult

for traditional snakes to evolve to concavities and sharp

corners. Xu et al. [27] proposed the use of GVF as an external

force in order to overcome the disadvantages of traditional

snakes. Since the GVF field is calculated as a diffusion of the

gradient vectors of an edge map derived from the image, it

greatly increases both the capture range of the snake and the

ability to move into boundary concavities.

J. Med. Biol. Eng., Vol. 31 No. 6 2011 408

The initial contour for a snake must be close to the desired

contour, otherwise it can converge to an incorrect resting place.

When the initial contour is very close to the real contour, the

number of iterations and convergence time can be reduced

significantly. According to the gray intensity distribution of the

optic disc, the retinal image is divided into two regions: one

with a defined optic disc, characterized as the obvious disc

boundary in the image with partial occlusions (Fig. 4(a)), and

one without a defined optic disc and without vague disc

boundary (Fig. 4(b)). For the well-defined optic disc image, the

binarization algorithm, edge detection algorithm, and

morphological filtering are used to obtain the initial contour.

The morphological filtering is used to fill small regions and

smooth the edge. For the not well-defined optic disc image, the

initial contour is initialized by choosing a circle which is close

to the desired contour.

The implementation of the GVF snake for detecting the

optic disc boundary is as follows.

Step 1: The Gaussian filter is applied to blur the image

( , ) ( , ) ( , )blurI x y G x y I x y .

Step 2: The edge image,2

( , ) ( , )blurg x y I x y , is generated.

Step 3: The GVF force field, ( , ) ( ( , ), ( , ))V x y u x y v x y , is

calculated. The GVF force field V is defined to

minimize the energy function. 2 2 2 2 2 2

( / / / / ) d du x u y v x v y g V g x y , where μ is a parameter controlling the degree of

smoothness of the vector field.

Step 4: The initial snake is placed close to the desired contour.

Snakes are curves that move within images to find disc

boundaries. The curve is represented by

( ) [ ( ), ( )], [0,1]X s x s y s s .

Step 5: The snake X(s) begins to deform driven by GVF forces.

The process is repeated until the snake becomes stable.

The snake deforms through the image to minimize the

energy function 1 2 21

20( ) ( ) ( ( ))extE X s X s E X s ds

,

where α and β are parameters representing the degree of

smoothness and tautness of the contour, respectively.

X'(s) and Χ"(s) are the first and second derivatives of

X(s) with respect to s, respectively. The external energy

Eext is derived from the image. The external force Fext is

derived from the external energy and defined so as to

attract the snake to strong edges. By replacing the

external force Fext by the GVF field V, a solution for the

GVF snake can be obtained.

(a) (b)

Figure 4. Retinal images (a) with a defined optic disc and (b) without a

defined optic disc.

2.4 Experimental design

The proposed method was applied to a set of

60 fluorescein images (36 images of healthy retinas and

24 images of retinas with disease). All the images, whose size

was 768 × 576 pixels, were captured using a retinal camera

(TOPCON TRC-50EX).

All the images were sent to experienced ophthalmologists,

who manually marked the disc boundary. The nearest distance

from the localized position p to the ground truth contour C was

measured to evaluate the localization algorithm. The nearest

distance to the ground truth contour (NDC) is defined as:

min , if int NDC( , ) 1

min , if ext

i

i

p q p Cp C i M

p q p C

(5)

where qi is an individual pixel on the ground truth contour C

and M is the amount of the pixel on the ground truth contour. If

NDC < 0, the localized position is in the ground truth contour.

If NDC > 0, the localized position is outside the ground truth

contour.

The boundary detection algorithm was evaluated using the

mean distance to the closest point (MDCP) [18,28]. The MDCP

is defined as:

1

1MDCP( , ) min 1 ,1

N

n i

n

S C s q i M n NN

(6)

where qi is an individual pixel on the ground truth contour C

and M is the amount of the pixel on the ground truth contour C.

sn is an individual pixel on the resulting contour S and N is the

amount of the pixel on the ground truth contour S. The smaller

the MDCP, the closer the resulting contour is to the ground

truth contour.

3. Results and discussion

3.1 Similarity in gray intensity

The average gray intensity of each optic disc and its inner

vessels for 10 test images are listed in Table 1. The gray

intensity difference between the optic disc and its inner blood

vessels may not be significant.

Table 1. Comparison of average gray intensity between the optic disc

and its inner vessels for 10 fluorescein images.

Image 1 2 3 4 5 6 7 8 9 10

Average gray intensity

of optic disc 182 218 133 143 112 182 201 150 155 173

STD of gray intensity of optic disc

28 25 29 26 31 26 35 23 21 33

Average gray intensity of inner vessels

203 243 146 159 131 191 242 171 163 197

STD of gray intensity

of inner vessels 13 17 9 11 16 12 21 12 16 15

Difference 21 25 13 16 19 9 41 21 8 24

3.2 Localization

The images resulted from the two top-hat operators, Otsu’s

threshold methods and subtraction operator are shown in Fig. 5,

where the difference between the two binary images is quite

Retinal Optic Disc Detection 409

obvious. The inner vessels of the optic disc are not segmented

after the top-hat operator with a small structuring element has

been applied.

(a) (b)

(c) (d)

(e) (f)

(g)

Figure 5. Localization of the optic disc. (a) Original image.

(b) Extracted optic disc region. (c) Top-hat transform with

large circular structuring element. (d) Top-hat transform with

small circular structuring element. (e) and (f) Otsu’s threshold

method performed on (c) and (d), respectively. (g) Result of

subtracting binary image (f) from binary image (e).

The parameter setting of the localization algorithm is

simple. The radius r should approach the half of maximum

width of the blood vessels. For the dataset used here, the

radius r was defined to be between 4 and 6, and the radius R

was defined to be between 10 and 12. In order to show the

localization accuracy, the results from all the key steps are

shown in Fig. 6. The first column shows the binary images

after the top-hat operator with a large structuring element and

Otsu’s threshold method had been applied. The second column

shows the binary images after the top-hat operator with a small

structuring element and Otsu’s threshold method had been

applied. The third column shows the binary images after

subtraction and morphological processing. The fourth column

shows the localization results. The last row is the localization

process of an image with very large lesion areas. Due to the

influence of the large lesions areas, the localized position

slightly deviates from the position of the optic disc.

For comparison, the localization method that uses the

vessels’ direction matched filter [11], that finds the vessel

branch with the most vessels [16], and that finds the largest

brightest connected object [3] were applied to the fluorescein

images. By definition, the localization is successful if

NDC < 10. Table 2 shows a statistical comparison among the

method proposed here and those proposed by Youssif et al.

[11], Kande et al., [16] and Walter and Klein [3]. The success

rates are 96.7%, 88.3%, 81.7%, and 53.3%, respectively. The

means of NDCs of successful cases are -39, -52, -57, and -65,

respectively, and the standard deviations of NDCs of

successful cases are 13, 17, 10, and 12, respectively. The

proposed method has the highest success rate but also the

largest mean of NDCs in the successful cases, indicating that

the approximate optic disc position obtained using the

proposed method cannot be considered as the center of the

optic disc.

Table 2. Performance comparison among various localization methods

using NDC.

Result

Method

Proposed method

Youssif et al. [11]

Kande et al. [16]

Walter et al. [3]

Success

(NDC<10 pixels) 58 53 49 32

Failure

(NDC10 pixels) 2 7 11 28

Success rate 96.7% 88.3% 81.7% 53.3%

Mean of NDCs for

successful cases -38 -42 -59 -50

STD of NDCs for successful cases

13 17 10 12

3.3 Boundary detection

The parameters for the preprocessing were set to m = 90,

h = 81, N = 50, and k = 0.7. The parameters for the GVF snake

were set to σ = 2.5, μ = 0.5, α = 2, and β = 1.5. All the boundary

detection results calculated using the proposed algorithm were

compared with the hand-labeled ground truth. Figure 7 shows a

comparison of the ground truth and the resulting contours

obtained from four images (three images with a defined optic

disc and one image without a defined optic disc). The first

image is from a well-defined optic disc. Due to the obvious

difference between the optic disc and the background, the

resulting contour and the ground truth are consistent. For the

second and third images, the optic disc of the images can be

considered as less defined. The detected boundary with a little

J. Med. Biol. Eng., Vol. 31 No. 6 2011 410

(a)

(b)

(c)

(d)

Figure 6. Several examples with proposed localization algorithm.

Figure 7. Several results obtained from proposed boundary detection method. The dotted line is the resulting contour and the solid line is the

ground truth.

distortion is slightly influenced by the burrs and small blood

vessels near the optic disc. For the fourth image, that without a

defined optic disc, the optic disc is almost concealed by the

background, indicating a significant difference between the

resulting contour and the ground truth.

The performance of the proposed algorithm was

evaluated using the sensitivity, specificity, and predictive

values [3]. The sensitivity represents the fraction of pixels

correctly classified as optic disc pixels. The specificity

represents the fraction of pixels erroneously classified as optic

disc pixels. The predictive value is the probability that a pixel

classified as belonging to the optic disc is actually part of the

optic disc. To further quantitatively test the performance of the

proposed approach, a series of experiments was carried out on

20 images with a defined optic disc and 10 images without a

defined optic disc from our database. Figure 8 shows the

sensitivity, specificity, and predictive values obtained using

the proposed method.

Figure 8. Sensitivity, specificity, and predictive values for 30 images (20

images with a defined optic disc and 10 images without a

defined optic disc).

Retinal Optic Disc Detection 411

Table 3 shows a performance comparison among methods

that use the GVF snake, the Hough transform, and the modified

active contour model [16] using MDCP, where MDCP < 5

indicates success. The success rates are 80%, 45%, and 65%,

respectively. The modified active contour model algorithm [18]

is not useful for fluorescein retinal images because the blood

vessels greatly affect its results. The modified active shape

model algorithm [22] is not suitable for distorted and irregular

retinal images, such as Fig. 2(c). The GVF snake has the

following advantages when used for fluorescein retinal images:

(1) it is suitable for all gray-level fluorescein images, including

low-contrast images, distorted and irregular images, and images

with an unclear optic disc; (2) it does not depend on the shape

and brightness of the optic disc; (3) the precise optic disc center

is not required.

Table 3. Performance comparison among various boundary detection

methods using MDCP.

Result Method

GVF snake Hough transform Kande et al. [16]

Success (MDCP<5 pixels)

48 27 39

Failure

(MDCP5 pixels) 12 33 21

Success rate 80% 45% 65%

4. Conclusion

Fast automated localization and boundary detection of the

optic disc can be valuable for optic disc examination. Most

fluorescein retinal images have low contrast and an unclear

optic disc. Conventional localization approaches are mainly for

color images and are very complex. The proposed method is

based on the similarity in gray intensity between blood vessels

and the optic disc. It uses mathematical morphology combined

with Otsu’s threshold algorithm. The experimental results show

that the proposed method outperforms other methods in terms

of success rate. The boundary of the optic disc is extracted by

the GVF snake after pixel-level preprocessing. The

preprocessing makes the approach robust to blood vessel

occlusions, ill-defined edges, noise, and fuzzy disc shapes. 80%

of disc boundaries were measured successfully. The average

sensitivity and predictive values of the boundary detection

method were 92% and 93%, respectively. For our future study,

clinical evaluation will be undertaken in order to integrate the

presented algorithm in a tool for the diagnosis of retina.

Acknowledgement

This study was supported by the National Natural Science

Foundation of China under grant 30970764.

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