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INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 9 September 2016

IJTC201609007 www. ijtc.org 467

Retinal Vessel Segmentation employing Neural

Network and Feature ExtractionNavpreet Kaur

Department of Computer Science and Engineering

Shaheed Udham Singh College of Engineering and Technology, Mohali, India.

Abstract: Diabetic retinopathy, Glaucoma, Hypertension are the most common sight threatening eye diseases due to the changes in

the blood vessel structure. The retinal blood vessel segmentation helps to identify the successive stages of a these diseases and thus

helps to treat them at early stages. Blood vessel segmentation by making use of multilayer perceptron neural network is one such

technique used for the segmentation of retinal blood vessels. As it provides the ability to identify and classify the image pixels as

vessels or non vessels automatically, but it fails to achieve high accuracies. It is unable to segment the vessels of varying width and

small size. Thus this research work provides the blood vessel segmentation with neural network, which gives more efficient results

on fundus images.

Keywords: Retinal blood vessel segmentation, Diabetic Retinopathy, Neural Network, Fundus Images.

I. INTRODUCTION

Diabetic Retinopathy, Glaucoma, Hypertension are the most

common sight threatening eye diseases. Diabetic

retinopathy (DR) is a result of long-term diabetes [1]. Major

vision loss due to DR is highly preventable with proper

screening and timely diagnosis at the earlier stages. The

various features of retinal vessels such as length, width,

length and branching pattern provide new techniques to

diagnose various diseases like diabetes, glaucoma, hyper-

tension, cardiovascular disease and stroke. Retinal images

provide valuable information related to human eye, by

which the vascular condition can be

accurately observed and analyzed. The only part of the

central circulation that can be viewed directly and analyzed

is the retinal vessel. Changes in blood vessel structure and

vessel distribution, caused by diabetic retinopathy can lead

to new vessel growth, which in turn instigates vision

impairment.

In human retina, one of the most important organs is the

optic nerve which acts as the convergent point of the blood

vessel net-work. The central retinal artery and central retinal

vein flow out through the optic nerve that supplies blood to

the upper layers of the retina. Besides, the optic nerve acts

as a channel to convey the information from the eye to the

brain. In early stages, most of the retinal pathologies affects

locally and does not distress the entire retina. But retinal

pathology on or near the optic nerve may severely affect the

vision even at the early stages because optic nerve is the

most essential part for vision. A few observations found in

several important retinopathies are attenuation changes,

focal narrowing and occlusion of retinal arteries. The

diameter and shape of a retinal vessel plays a key role as

indicators in ophthalmologic studies. These changes give

valuable information to identify the successive stages of

diseases and their response to various therapies. The optic

nerve can be observed in a close view of the retinal fundus,

where the optic disc is the portion of the nerve that is visible

or perceivable by the eye. Fundus imaging is one of the

popular clinical procedures available to record this close

view observations of the retina. This fundus imaging

procedure is also used for the diagnosis and evaluation of

the healthy and non-healthy retinas of human eye. In a

healthy retina the optic nerve has a standard identifiable

size, shape, color and location relative to the blood vessels.

Nevertheless, in a retina containing lesions, any one or more

of these properties may be deviated from its standard level

and show a large variance. At various stages of the disease,

the vascular network in retina is very much affected and

hence various morphological changes occurring retinal

vessels.

We can substantially observe enough geometrical changes

in diameter, branching angle, length in the retinal blood

vessels due to diseases. The segmentation and measurement

of retinal blood vessels can be used to grade the severity of

certain diseases. The sign of risk level for diabetic

retinopathy is the variation in width of retinal vessels within

the fundus. One of the most important tools for the

prediction of proliferate diabetic retinopathy is the abnormal

variation in diameter along the vein. Moreover, the various

retinal micro vascular abnormalities predicted are seen to be

the early symptoms for the risk of stroke. In all these cases,

the desired focus is on the variation in diameter of the

vessel and not in the exact diameter of the vessel. An

alternative application of retinal vessel segmentation is

biometric identification using distinctive retinal vascular

network for each individual. The rest of the paper is

structured as: In Section 2 Related Work is described. In

section 3 Methodology of Proposed work is defined. In

Section 4 we give Comparative study between existing and

proposed approach and finally in Section 5 we give

Conclusion to paper.

II. RELATED WORK

Blood vessel segmentation in retinal images is attained by

classifying image pixels as vessel or non vessel based on

the local image features. In general there are two basic

approaches for blood vessel segmentation in retinal images.

The algorithms used for the segmentation of blood vessels

are broadly classified as pixel processing based methods

and vessel tracking methods.

Pixel processing-based methods normally consist of two

phases. In the first phase, an enhancement procedure is

implied and it selects an initial set of pixels, which is further

ensured as vessels in the second phase [9]. The retinal

vessel segmentation method presented in Ref. [2] consists

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of three processing phases. In the first phase, background

normalization of monochromatic input image is performed

and later thin vessel enhancement procedures are used. In

the second phase, the vessel centerline candidate points are

selected and subsequently these points are connected and

based on vessel length, the validation of centerline segment

candidates is achieved. In the third phase, vessels with

different widths are enhanced and processed using binary

morphological reconstruction technique and vessel filling is

achieved using region growing process. Soares et al. have

proposed an algorithm, where retinal blood vessels are

detected using Gabor wavelets by representing each pixel

by a feature vector and then by using Bayesian classifier

with Gaussian mixtures, each pixel is classified as either a

vessel or non vessel pixel and thus segmentation is

achieved. The concept of matched filter detection was

proposed by Chaudhuri et al. [10], where twelve rotated

versions of 2-D Gaussian shaped templates are used to

search vessel segments along all possible directions. The

resultant image produced is the binary representation of the

retinal vasculature. Likewise, segmentation of retinal

vessels are also obtained by matched filtering approaches

using global [11] orlocal thresholding strategies [12]. Also

for the purpose of vessel borders extraction, differential

filters based on either first-or-second order derivatives are

used. A two stage region growing procedure was proposed

by Martinez-Perez et al. where features derived from image

derivatives are used in the segmentation of retinal vessels

[13,14]. As stated in Ref. [15], the edge detection, matched

filtering and region growing procedures can also be

collectively used for the automated detection of retinal

blood vessels. Jiang et al. have proposed a technique of

adaptive local thresholding based on the use of verification-

based multithreshold scheme combined with classification

procedures in Ref. [16], used for the detection of vessels in

retinal images. A technique used for the segmentation of

vessel like patterns in retinal images that combines

morphological filters and cross-curvature evaluation was

proposed by Zana et al.in Ref. [17]. In this approach, vessel

segments alone are considered as image feature and hence

using morphological filters simplifies the computation of

cross-curvature. An algorithm for retinal vessel

segmentation based on the classification of pixels using

simple feature vector was proposed by Niemeijer et al. in

Ref. [18]. A new method of segmentation of blood vessels

in retinal images was pro-posed by Staal et al. in Ref. [19].

This supervised method is called primitive-based method

and this algorithm is based on the extraction of image ridges

used to compose primitives that describe the linear

segments called line elements. The pixels those are assigned

to the closest line element partitions the image in the form

of image patches and are classified using a set of features

from the corresponding line and image patch. In addition, a

technique based on neural network is used to identify the

retinal blood vessels, where the inputs are obtained using

principal component analysis and then edge detection

technique is used.

The classifiers are trained by learning from manually

labeled images. The classifiers are the Bayesian classifier

[17], KNN method [18], support vector machines [19] and

neural networks [20]. This paper provides an approach

based on the supervised method of segmentation using

neural network.

III. PROPOSED METHOD

This research work proposes a retinal vessel segmentation

technique using Artificial Neural Networks (ANN). Retinal

vessels are identified by using a multilayer perceptron

neural network, for which the inputs are derived from

Gabor and moment invariants based features.

The technique used in this method is tested and evaluated

by making use of the retinal color images available on

DRIVE database. This database has been widely used as

ground truth for performance evaluation by other

researchers to test their vessel segmentation methodologies.

The retinal images available in this database were captured

using a Canon CR5 nonmydriatic3CCD camera with a

45◦Field-of-View (FOV), in digital form. The DRIVE

database consists of forty eye fundus color images which

are divided into training and test set, each set containing

twenty images. The color images of the retina available in

the database are of size768 × 584 pixels.

A. PREPROCESSING

The first step is the preprocessing of image for further

processing. The RGB features of the image are extracted in

this stage as shown in Figure 1. The true color RGB is

converted to the grayscale intensity image to evaluate its

adequate ability for the segmentation of the retinal blood

vessels. The conversion is done by eliminating the hue and

saturation information, while retaining the luminance.

In the next step, the retinal image is further processed and

smoothened using Gaussian filter and mean filter as shown

in figure 2. After that it is converted into grayscale image

for further processing.

Then the Gabor features are extracted at different

orientations as shown in figure 3. Gabor filtering is done

and as per the Gabor coefficient attained, the original image

is convolved. Next, the moment invariant features are

extracted as shown in figure 4. Now the samples are taken

from the vessel and non vessel regions and they are trained

using neural network.

B. NEURAL NETWORK TRAINING

Each pixel in the retinal image is classified as a vessel or a

non vessel pixel, using neural network. The computational

model of neural network has similarities with the human

visual system. In order to classify each pixel of the retinal

image as vessel or not, a multilayer perceptron neural

network is used. It can be described as a black box having

multiple inputs and multiple outputs which operates using a

large number of mostly parallel connected simple arithmetic

units.

The back propagation algorithm is used in layered feed-

forward ANNs. The network receives inputs by neurons in

the input layer, and the output of the network is given by the

neurons on an output layer. There may be one or more

intermediate hidden layers. The back propagation algorithm

uses supervised learning, which means that we provide the

algorithm with examples of the inputs and outputs we want

the network to compute, and then the error (difference

between actual and expected results) is calculated. The idea

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of the back propagation algorithm is to reduce this error,

until the ANN learns the training data. The training begins

with random.

FIGURE 1. RGB COMPONENTS OF A TRUE COLOR RETINAL IMAGE.

FIGURE 2. GRAY SCALE IMAGE OBTAINED AFTER APPLYING GAUSSIAN AND MEAN FILTER.

FIGURE 3. EXTRACTION OF GABOR FEATURES

FIGURE 4. EXTRACTION OF MOMENT INVARIANT BASED

FEATURES.

Weights , and the goal is to adjust them so that the error will

be minimal.

A. ALGORITHM

Neural network using back propagation algorithm is

applied, using 5/6th of the data for training and 1/6th of the

data for validation. The training algorithm used in the back

propagation network is as follows:

Step 1 – Read the input image.

Step 2 – Separate the RGB components in R, G, and B

different planes.

Step 3 – Morphological opening operation is performed to

fill the vessel discontinuities.

Step 4 – Preprocessing steps like mean filtering and

Gaussian filtering are performed.

Step 5 – RGB features are extracted for the preprocessed

image and its mean is calculated.

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Step 6 – Gray scale image is obtained for further

processing.

Step 7 – Gabor features are extracted at different

orientations.

Step 8 – Moment invariants-based features are extracted.

Step 9 – Samples are taken from vessel and non vessel

regions and they are trained using neural networks.

Step10 – Segmentation of blood vessels is obtained as

output.

For each training pair, the neural network performs the

following tasks. At the first stage, during the initialization

of weights, a few small random variables are assigned. In

the next stage, i.e. feed forward stage, each input unit (Xi)

receives an input signal and transmits this signal to all the

units in the layer above i.e. hidden units, Each

hidden unit calculates the activation function,

( ) ∑

(1)

is the bias on hidden unit j. Each hidden unit sends this

signal to all output units. Each output unit then sums its weighted input signals and applies

its activation function to calculate the output signals.

∑ (2)

is the bias on output unit k.

During back propagation of errors, each output unit

compares its calculated activation with its target value , to

find out the related errors for that pattern. Error information

term is given as,

(3)

It is used to distribute the error at the output unit back to

all units in the previous layer. Then each hidden unit

( ) sums its delta inputs from units in the

above layer as:

∑ (4)

Similarly the error information term is

computed for each hidden unit. During the final stage, the

weights and biases are updated using the factor and the

activation function is updated, using as the learning rate.

Each output unit updates its weights and biases by using

theweight correction term and bias correction

term ,

Where,

Therefore,

(5)

Similarly the weights and biases are updated for each

hidden unit. Finally, the stopping condition is tested. The

stopping condition may be minimization of the errors,

number of epochs etc.

FIGURE 5. SEGMENTED IMAGE.

IV. RESULT ANALYSIS

The automatic vessel segmentation approach used in this

paper was tested on images from DRIVE database. This

technique has been proved as one of the most valuable tools

for the segmentation of blood vessels in retinal images.

The proposed algorithm is tested for various images having

the size 768 × 584 pixels are used for experimentation. The

results of different matrices of performance that is accuracy,

sensitivity and specificity on these images are used to verify

the performance of proposed algorithm.

A. PERFORMANCE EVALUATION

The accuracy was estimated by determining the true

positive fraction, which is the ratio of the number of true

positives to the total number of vessel pixels in the ground

truth segmentations. The ratio of the number of false

positives to the total number of non vessel pixels in the

ground truth segmentations determines the false positive

fraction. A pair formed by a true positive fraction and a

false positive fraction is plotted on the graph, which

produces a curve as shown in Fig In our experiments, these

fractions are calculated over all test images, considering

only pixels inside the field of view. The manual

segmentation result provided along with each database

image seems to be the best standard for evaluating the

performance measures. To exhibit the performance by

employing Gabor features and moment invariants-based

features using neural networks, we present the results

obtained by our method and compared them with the results

obtained by other existing methods, involving matched

filters and adaptive local thresholding techniques. The

Gabor feature extraction at different orientations used in

this method increases the accuracy of the segmentation of

vessels with different diameters. This method proves itself

to be efficient in enhancing the vessel contrast, thereby

filtering the noise. We have used a straight forward

MATLAB implementation for testing the images from

DRIVE database and we have obtained the best per-formed

vessel segmentation results.

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FIGURE 6. ROC CURVE FOR THE CLASSIFICATION ON THE

DRIVE DATABASE USING FEATURE EXTRACTION

V. CONCLUSION

This thesis has overviewed the method for retinal Image

segmentation using neural network. It automatically tracks

and segments the vessels in fundus images. One of the

highlights of this proposed technique is its adaptability to

particular image intensity properties. It assigns a precise

classification result as vessel or non-vessel to each pixel,

compared to other vessel segmentation algorithms. This

retinal vessel segmentation technique gives knowledge

about the location of vessels which paves a way for the

screening of diabetic retinopathy. One of the practical

applications of this method is to apply the results of this

segmentation algorithm for providing better performance

analysis in computer-aided diagnosis system for retinal

images. This allows us to analyze large number of images

and to characterize many more properties of the retinal

vasculature. The demonstrated effectiveness, together with

its simplicity, makes this automated blood vessel

segmentation method a suitable tool for the complete

prescreening system for early diabetic retinopathy detection.

While assessing the obtained segmentation results, a few

limitations like partial missing of very thin vessel branches

are noticed. These can be obviously noticed only in very

few image as a consequence of the variability of intensity

and contrast among vessels and the other regions. It can be

compensated by using vessel enhancement procedure. Such

misdetections can be minimized by using more flexible

classification process for every image point and thereby, the

overall performance of this method can be improved

REFERENCES

[1] M. Garcia, C.I. Sanchez, M.I. Lopez, D. Abasolo, R.

Hornero, Neural network based detection of hard

exudates in retinal images, Computer Methods

Programs Biomed. 93 (2009) 9–19.

[2] A.M. Mendonc¸ a, Aurélio Campilho, Segmentation of

retinal blood vessels by combining the detection of

centerlines and morphological reconstruction, IEEE

Med. Imaging 25 (9) (2006) 1200–1213.

[3] A.J. Frame, P.E. Undrill, M.J. Cree, J.A. Olson, K.C.

McHardy, P.F. Sharp, J.V. Forrester, A comparison of

computer based classification methods applied to the

detection of microaneurysms in ophthalmic fluorescein

angiograms, Computer Biol. Med. 28 (3) (1998) 225–

238.

[4] M. Larsen, J. Godt, N. Larsen, H. Lund-Andersen, A.K.

Sjølie, E. Agardh, H.Kalm, M. Grunkin, D.R. Owens,

Automated detection of fundus photographic red

lesions in diabetic retinopathy, Invest. Ophthalmic. Vis.

Sci. 44 (2) (2003)761–766.

[5] C. Oyster, The Human Eye: Structure and Function,

Sinauer Associates, Sunder-land, MA, 1999.

[6] A. Hoover, M. Goldbaum, Locating the optic nerve in a

retinal image using the fuzzy convergence of the blood

vessels, IEEE Trans. Med. Imaging 22 (8) (2003)951–

958.

[7] C. Sinthanayothin, J.F. Boyce, H.L. Cook, T.H.

Williamson, Automated localization of the optic disc,

fovea, and retinal blood vessels from digital color

fundus images, Br. J. Ophthalmol. 83 (8) (1999) 902–

910.

[8] C. Sinthanayothin, J.F. Boyce, T.H. Williamson, H.L.

Cook, E. Mensah, S. Lal, D.Usher, Automated

detection of diabetic retinopathy on digital fundus

images, Diabet. Med. 19 (2) (2002) 105–112.

[9] K.A. Vermeer, F.M. Vos, H.G. Lemij, A.M. Vossepoel,

model based method for retinal blood vessel detection,

Comput. Biol. Med. 34 (2004) 209–219.

[10] S. Chaudhuri, S. Chateterjee, N. Katz, M. Nelson, M.

Goldbaum, Detection of blood vessels in retinal images

using two-dimensional matched filters, IEEETrans.

Med. Imaging 8 (3) (1989) 263–269.

[11] T. Chanwimaluang, G. Fan, An efficient algorithm for

extraction of anatomicalstructures in retinal images, in:

Proc. ICIP, 2003, pp. 1193–1196.

[12] A. Hoover, V. Kouznetsova, M. Goldbaum, Locating

blood vessels in retinal images by piecewise threshold

probing of a matched filter response, IEEE Trans. Med.

Imaging 19 (3) (2000) 203–211.

[13] M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A.

Thom, A.A. Bharath, K.H.Parker, Segmentation of

retinal blood vessels based on the second directional

derivative and region growing, in: Proc. ICIP, 1999, pp.

173–176.

[14] M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A.

Thom, A.A. Bharath, K.H.Parker, Scale-space analysis

for the characterization of retinal blood vessels, in

Medical Image Computing and Computer- Assisted

Intervention—MICCAI’99,C. Taylor and A.

Colchester, (Eds.), Springer: New York, Lect. Notes

Comput. Sci.16794 (1999) 90–97.

[15] Y. Wang, S.C. Lee, A fast method for automated

detection of blood vessels in retinal images, in: IEEE

Comput. Soc. Proc. Asilomar Conf., 1998, pp.1700–

1704.

IJTC.O

RG


ISSN-2455-099X,



[16] X. Jiang, D. Mojon, Adaptive local thresholding by

verification based multi-threshold probing with

application to vessel detection in retinal images,

IEEETrans. Pattern Anal. Mach. Intell. 254 (1) (2003)

131–137.

[17] F. Zana, J.-C. Klein, Segmentation of vessel-like

patterns using mathematical morphology and curvature

evaluation, IEEE Trans. Med. Imaging 11 (7)

(2001)1111–1119.

[18] M. Niemeijer, J. Staal, B. van Ginneken, M. Loog,

M.D. Abràmoff, Comparative study of retinal vessel

segmentation methods on a new publicly available

database, in: M. Fitzpatrick, M. Sonka (Eds.), Proc

SPIE Med. Image., vol. 5370,2004, pp. 648–656.

[19] J. Staal, M.D. Abramoff, M. Niemeijer, M.A.

Viergever, B. van Ginneken, Ridge-based vessel

segmentation in color images of the retina, IEEE Trans.

Med. Imaging 23 (4) (2004) 501–509.

[20] L. Zhou, M.S. Rzeszotarski, L.J. Singerman, J.M.

Chokreff, The detection and quantification of

retinopathy using digital angiograms, IEEE Trans.

Med. Imaging 13(4) (1994) 619–626.

[21] Y. Tolias, S.M. Panas, A fuzzy vessel tracking

algorithm for retinal images based on fuzzy clustering,

IEEE Trans. Med. Imaging 17 (2) (1998) 263–273.

[22] A. Can, H. Shen, J.N. Turner, H.L. Tanenbaum, B.

Roysam, Rapid automated tracing and feature

extraction from retinal fundus images using direct

exploratory algorithms, IEEE Trans. Inf. Technol.

Biomed. 3 (2) (1999) 125–138.

[23] O. Chutatape, L. Zheng, S.M. Krishnan, Retinal blood

vessel detection and tracking by matched Gaussian and

Kalman filters, in: Proc. 20th Annu. Int. Conf.

IEEEEng. Med. Biol., 1998, pp. 3144–3149.

[24] V.S. Lee, R.M. Kingsley, E.T. Lee, et al., The

diagnosis of diabetic retinopathy. Ophthalmoscopy

versus fundus photography, Ophthalmology 100

(1993)1504–1512.

[25] Research Section, Digital Retinal Image for Vessel

Extraction (DRIVE) Database, Univ. Med. Center

Utrecht, Image Sci. Inst., Utrecht, The Netherlands,

2014.

[26] R.C. Gonzalez, R.E. Woods, Digital Image Processing,

229–237, Addison-Wesley, Reading, 1993, pp. 583–

586.

[27] D.L. Toulson, J.F. Boyce, Segmentation of MR images

using neural nets, in: Proc.Br. Mach. Vision Conf.,

1991, pp. 284–292.

[28] D.L. Toulson, J.F. Boyce, Segmentation of MR images

using neural nets(expanded version), Image Vis.

Comput. 10 (1992) 324–328.

[29] S.C. Lo, M.T. Freedman, J.S. Lin, et al., Automatic

lung nodule detection using profile matching and back-

propagation neural network techniques, J. Digit.

Imaging 6 (1993) 48–54.

[30] M.L. Astion, P. Wilding, The application of back

propagation neural networks to problems in pathology

and laboratory medicine, Arch. Pathol. Lab. Med.

116(1992) 995–1001.

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