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American Journal of Public Health Research
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The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

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The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
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Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

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The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

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A Comparative Study of Retinal Vasculature Extraction in Digital Fundus Images

Islam Abdul-Azeem Fouad

Biomedical Technology Department, SALMAN BIN ABDUL-AZIZ, Al-Kharj, K.S.A

Abstract: Some of the most common blinding conditions are caused by choroidal neovascularization (CNV). The relevant conditions include diabetic retinopathy and age-related macular degeneration. At present, the only proven modality of effective treatment is the application of laser energy to the CNV to cauterize the vessels. The key to effective and lasting treatment is the identification of the full extent of the CNV, complete cauterization of the CNV by accurately aiming an appropriate amount of optical energy while ensuring that healthy tissue is not cauterized. Extraction techniques must be developed to discern the retinal blood vessels tree and determine the positions of laser shots in a reference frame. This paper presents an efficient comparison of different methods to segment blood vessels, which is a prominent anatomical structure in retina, in both gray-scale and color retinal images. The blood vessel extraction is composed of six algorithms according to two criteria, i.e., Extraction of the blood vessel boundaries (using Difference operators, Decision based-directional edge detection, Morphological gradient and Deformable model algorithm) & Extraction of the core area of the blood vessel tree by tracing vessels centers (using 2-dimensional matching filters and Morphological reconstruction algorithm). Results on various retinal images verify the effectiveness of the proposed methods.

Keywords: segmentation, retinal image, vessel extraction, morphology, deformable model

Received December 05, 2014; Revised February 04, 2015; Accepted February 09, 2015

Citation: Islam Abdul-Azeem Fouad, A Comparative Study of Retinal Vasculature Extraction in Digital Fundus Images. Journal of Biomedical Engineering and Technology, 3 (1). 1-7. January 2015. doi:10.12691/jbet-3-1-1

1. Introduction

Age-related macular degeneration (AMD) is the main reason for often severe loss and lasting decrease in visual acuity. Choroidal neovascularization (CNV) associated with age-related macular degeneration leads to irreparable damage in structure and function of retinal tissue [1]. The therapeutic approach favors timely and complete photocoagulation of the entire peripheral retina in order to save central vision [2,3]. Several thousand lesions are required and care is taken to avoid radiating the macula, the optic disc, the retinal blood vessels, and the region between the macula and the optic disc containing the nerve fibers to the area of acute vision (the macula). For each eye, this procedure requires several thousands of laser shots. This could be achieved in several hours spaced over many treatment sessions [4]. The full extent of the affected peripheral retina must be completely coagulated while ensuring that healthy tissue is not cauterized. Despite the superiority of laser treatment over other available methods, serious problems remain. The current rate of success of this procedure is less than 50% following one treatment session with a recurrence and or persistence rate of about 50% [5,6]. The latter condition requires repeating the treatment. Each treatment repetition in turn has a 50% failure rate. The visual recovery declines with each successive treatment. Indeed, several studies indicate that incomplete treatment was associated with poorer prognosis than no treatment [7]. Complete treatment can be obtained if the cauterization is done in one session. So, we assume to have a computerized system to track the retina and make the whole number of the required laser shots in one session and the system have a computer that captures the retinal images from a fundus camera, perform some image processing algorithms to these images to finally feed the positions to which the laser will be directed to a beam steering apparatus. A primary step is to design the image processing algorithms required to segment the retinal images. The goal of the segmentation step is to extract the sensitive objects in the retinal images which include: the blood vessel tree, the macula, the optic disc ,and the region between them. The locations to which laser will be directed are to be determined away from these sensitive objects.

In this work, we present comparisons of applying some segmentation algorithms to both gray-scale and color retinal images. Our interest is to auto detect and extract blood vessels in the retinal images.

The blood vessel segmentation can be classified into two categories:

•  Extraction of blood vessel boundaries and

•  Extraction of the core area of the blood vessel tree by tracing vessel centers.

The first category relies on detecting vessel edges by several techniques. Among those techniques we use difference operators and simple thresholding, edge detection based on statistical parameters calculated from the image [8], edge detection by morphological gradient and detecting the edges using deformable models. In the second category, the goal is to extract the core of the blood vessels. We introduce three methods whereby, the core area of the blood vessel tree could be extracted: extraction of the blood vessel tree using 2-D matched filters and extraction of the blood vessel tree using morphological reconstruction.

This paper is organized as follows: a brief introduction is presented in Section 1. Section 2 summarizes the theory and algorithms of segmentation of blood vessels from the fundus images. Section 3 describes the results & discussion of the applied algorithms on both gray-scale and color retinal images. Conclusions work form Section 4.

2. Methods

A. Boundary Detection by Difference Operators

An edge is the boundary between two regions with relatively distinct gray level properties. So, the magnitude of the gradient of an image at certain point indicates the presence of an edge at that point [9].

In this work, we use the “Sobel”& “Canny” operators to segment the blood vessel tree. For each of these operators the gray-scale image is convolved by the operators in the x and y directions, then the magnitude is calculated. The points of magnitude greater than certain threshold are taken to be the edge points or the blood vessel contours. For the color image, we separate it into its three components (R,G, and B) then using the same previously described algorithm to each component.. Finally, ORing the obtained results is applied (performing OR operation).

B. Boundary Detection Using Image Statistics

The image statistics; such as the mean and the standard deviation can be used in boundary detection. The algorithm called DBDED which stands for decision-based directional edge detector is used for automatic target recognition (ATR) based on the image statistics. In this work, it is used to detect the boundaries of the blood vessel tree in the retinal images [8].

For the color image, we apply the algorithm in a per-frame basis by separating it into its three components (R,G&B), then using the previously described algorithm to each component ,and finally ORing the results (performing OR operation).

C. Boundary Detection Using Deformable Models

The most known procedure that were used to detect the blood vessel boundaries and cores were presented as mentioned before. The recent one of these methods is contour detection using deformable models or snake. [11,12].

For the gray-scale images, we apply the algorithm described in [13] (See algorithm in Appendix (A.1) for the pseudo-algorithm). In the case of color image, we apply the algorithm in a per-frame basis by separating it into its three components (R,G and B) then the same algorithm is used to each component ,and finally ORing the results (performing OR operation).

D. Boundary Detection Using Morphological Methods

The mathematical morphology can be used as a tool of extracting image components that are useful in the representation and description of region shape, such as boundaries and skeleton. The morphological dilation and erosion of two images f & b denoted by and respectively, are the two basic morphological methods upon them all of the tasks achieved by image morphology depend. The morphological gradient of an image can be computed using the dilation and erosion as follows:

[picture] | (1)

For the color image, we apply the (Erosion & Dilation) in a per-frame basis then the subtraction is done.

E. Extraction of the blood vessel tree using 2-D matched filters

Blood vessels have a special property that the two edges of a vessel always run parallel to each other. Such objects may be represented by piecewise linear directed segments of finite width [10]. The 1-D Gaussian filter is:

To get the kernel that will be used to detect segments along the direction of an angle θ, the vertical kernel must be rotated using a rotation matrix given by:

[picture] | (2)

The value at the position (x, y) in the vertical kernel will be in the position (u, v) in the kernel rotated by an angle θ when the kernel is rotated around its center. Assuming an angular resolution of 15°, we need 12 different kernels to span all possible orientations. The retinal image is convolved by the 12 different filters one at a time and at each pixel, only the maximum of their responses is retained.

For the color image, an algorithm in a per-frame basis is used by separating it into its three components (R,G and B), then using the previously described algorithm to each component, and finally ORing the results (performing OR operation).

F. Extraction of the blood vessel tree using morphological reconstruction

As mentioned before, mathematical morphology can be used in many of image processing applications. By using morphological reconstruction, the connected components of an image can be extracted, which are marked in another image even by one point. The pseudo algorithm is shown in Appendix (A.2).

For the color image, an algorithm in a per-frame basis is applied by separating it into its three components (R,G&B) then using the previously described algorithm to each component, and finally ORing the results (performing OR operation).

3. Results and Discussion

A. Data Acquisition System

The retinal images used were acquired using a TOPCON TRC-50X fundus camera. All images collected were acquired with the camera adjusted at 50° mode (this corresponds to the field of the eye seen by the camera) and with the red-free illumination mode. A Sony Charge Coupled Device (CCD) video camera was attached to the eyepiece of the fundus camera to make the images collected using the fundus camera available in standard video format. The video output from the CCD camera was interfaced to a Micron PC (866 MHz processor, 256Mbytes of RAM) through a video digitizer card. The retinal images captured by this setup may be one of the three possible types. The first type is gray level images in the normal case, (i.e., no injected dye). In this type of images, the blood vessels appear as dark objects on a bright background. The second type is gray level images with brighter blood vessels relative to a darker background. This type of images is obtained when the patient is injected with the IndoCyanine Green dye. The third type is the colored images that appear with the true colors of the retina. The images were captured to the memory of the computer system by means of a software package called Image Net 2000 (version 2.03(Topcon corp. 96-98)). This package can capture either individual frames or real time video sequences. The size of the frame is 640x480 pixels of either colored or gray level images. The images were saved on a hard disk for further processing..

Figure 3.1.a, b and c show a normal image, an image with the patient injected with an IndoCyanin Green dye and a color images respectively.

Figure 3.1. Different types of retinal images

Figure 3.2. Results of applying “Sobel” operators

B. Results of applying Difference operators

The results of applying the “Sobel” operator on the images shown in Figure 3.1 are shown in Figure 3.2.a, b and c respectively. In the image shown in Figure 3.2.a, the threshold used is equal to the maximum of the “Sobel” magnitudes divided by 1.5. A threshold of value equal to the maximum of the “Sobel” magnitudes divided by 2 was used for the image shown in Figure 3.2.b. The values of 1.5 and 2 are optimized for the accuracy and the noise detection. The gradient result of the color image and the image formed by subtracting that gradient from the original color image are shown in Figure 3.2.c and d respectively.

The results of applying the canny operator on the images shown in Figure 3.1 are shown in Figure 3.3.a., b and c. respectively. In the image shown in Figure 3.3.a., the threshold vector used is equal to [0.05 0.12]. A threshold vector [0.09 0.2] was used for the image shown in Figure 3.3.b. The image shown in Figure 3.3.c. was obtained by applying the canny operator, with a threshold vector equal to [0.03 0.09], on the image shown in Figure 3.1.c. in a per-frame basis ,and finally ORing the results (performing OR operation).

Figure 3.3. Results of applying “Canny” operator

Detecting the boundaries of the blood vessels using “Sobel” method in the gray-scale image has been shown to be unreliable due to the existence of small vessels that are not well defined or having poor edges. To extract these small vessels, some of the false edges and noises in the background appear which limits the accuracy of the process. Using the same method to detect the boundaries of the blood vessels within the color image gives better results due to the decrease of the false edges and noise in the background, which gives good appearance of the segmented blood vessels compared to the results in the gray-scale image case. Unlike “Sobel” method, results of applying the “Canny” edge detector on both the gray-scale and color retinal images gives almost no noise and very pure background making the segmented vessels appears so soft.

C. Results of applying Image statistics

The results of applying this algorithm on the images in Figure 3.1 are shown in Figure 3.4.a, b and c. respectively.

The result shown in Figure 3.4.c are obtained by separating the color image shown in Figure 3.1.c into its three components (R,G, and B) then using the proposed decision - based directional edge detection algorithm to each component, and finally ORing the results (performing OR operation).

It is clear from the results that the same problems found when working with gray-scale and color images: false edges cannot be deleted completely. Also, to obtain the boundaries of the small vessels, more false edges appear. Moreover, the detection of boundaries using image statistics does not perform well when illumination is non-uniform (regions a, b, and c in Figure 3.4.a, 3.4.b, and 3.4.c respectively) because of variations in the intensity of some blood vessels compared to the image background.

Figure 3.4. Results of applying the proposed DBDED algorithm

D. Results of applying Deformable Models

The “snakes” algorithm extracts the contour of the blood vessel tree with minimal amount of noise as it clear from Figure 3.5 because the seed contours initiated away from a true edge shrinks until vanishing. This leads to the extraction of true contour while neglecting the others because of the threshold used is related to the maximum edge value. In the case of color retinal image shown in Figure 3.1.c, the result shown in Figure 3.5.e and 3.5.f obtained by separating the color image into its three components (R,G, and B) then calculating the external energy term (blood vessels boundaries) to each component and taking the maximum gradient at each point from the three frames as the external energy term (at each point take the maximum response of applying Sobel gradient for each of the three frames) and finally the pre-described Snake algorithm will be applied.

This algorithm extracts the contours of the wide vessels well, while the problem of extracting the small vessels still remains.

Figure 3.5. Results of applying “Deformable Models” algorithm

E. Results of applying Morphological gradient

The results of computing the morphological gradient of the images in Figure 3.1.a and b then taking the points that pass certain threshold are shown in Figure 3.6.a and b respectively. Figure 3.5.c shows the result of the algorithm on the color image shown in Figure 3.1.c.

In the gray-scale image the same problem of obtaining false edges in the background also exists.

Figure 3.6. Results of applying “Morphological Gradient” algorithm

F. Results of applying two dimensional matching filters

Figure 3.7.a, b and c show the results of applying this algorithm on the normal retinal image, the ICG image and the color images shown in Figure 3.1.a, b and c respectively.

In both gray-scale and color images this method gives reasonable results but it takes too much time and computation effort.

Figure 3.7. Results of applying “2-dimensional matching filters” algorithm

G. Results of applying morphological reconstruction

The results of extracting the blood vessel tree in a normal image, an ICG image and a color image using morphological reconstruction are shown in Figure 3.8, Figure 3.9 and Figure 3.10 respectively. The original images shown in Figure 3.1, were convolved by a one-dimensional Gaussian filter in the vertical and in the horizontal directions. The points that pass a small threshold were taken to compose the mask image, and the points that pass a higher threshold were taken to compose the marker image. It is clear from the Figure s that the mask images contain much more information than the required (the connected blood vessel tree and others), but the marker images contain only points that really belong to the blood vessel tree.

From the presented results, it was found that applying the morphological reconstruction algorithm gives reconstructed images with pure segmented vessels and high clear background appearance in both gray-scale & color images. On the other hand this algorithm takes too much time and computation effort.

Figure 3.8. Reconstruction of normal retinal image

Figure 3.9. Reconstruction of ICG retinal image

Figure 3.10. Reconstruction of color retinal image

From the previous presented results, the following points were extracted:

•  False edges and noise clearly were appeared in the results of “Sobel operators”, “Image Statistics”, and “Morphological Gradient” algorithms shown in Figure 3.2, Figure 3.4 and Figure 3.6 respectively. On the other hand the four other methods give little noise appearance and almost clear background shown in Figure 3.3, Figure 3.5, Figure 3.7, Figure 3.8, Figure 3.9 and Figure 3.10.

•  The non-uniform illumination which was caused from variations in the intensity of some vessels compared to the background was clearly affect the vessels segmented using “Image Statistics” method. So, extracting the boundaries by this method may not lead to good result compared to other proposed methods as “Canny operator” and “2- dimensional matching filters” where all of the sensitive objects are segmented accurately and the background is clear.

Table 3.1 shows the time taken to segment vessels shown in the previous segmented results. From the table, it’s obvious that generally working with color images needs more computational efforts than gray-scale images.

Table 3.1. segmentation time data

4. Conclusion

Finally, some techniques for automatic segmentation of blood vessels tree in different types of retinal images were demonstrated. The obtained results so far suggest that: applying segmentation techniques on gray-scale images is easier than applying those on color images as in the color images the algorithm is applied to the three R, G, and B color image components, and so the time taken and computational effort in the color images making the process harder. On the other hand, the results presented on the sample retinal images give almost the same vessels appearance and background clearance on both cases.

References

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