Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust...

ORIGINAL ARTICLE

Integrating adaptive neuro-fuzzy inference system and localbinary pattern operator for robust retinal blood vesselssegmentation

Abdolhossein Fathi • Ahmad Reza Naghsh-Nilchi

Received: 12 June 2011 / Accepted: 25 July 2012 / Published online: 9 August 2012

� Springer-Verlag London Limited 2012

Abstract Automatic extraction of blood vessels is an

important step in computer-aided diagnosis in ophthal-

mology. The blood vessels have different widths, orienta-

tions, and structures. Therefore, the extracting of the proper

feature vector is a critical step especially in the classifier-

based vessel segmentation methods. In this paper, a new

multi-scale rotation-invariant local binary pattern operator

is employed to extract efficient feature vector for different

types of vessels in the retinal images. To estimate the

vesselness value of each pixel, the obtained multi-scale

feature vector is applied to an adaptive neuro-fuzzy infer-

ence system. Then by applying proper top-hat transform,

thresholding, and length filtering, the thick and thin vessels

are highlighted separately. The performance of the pro-

posed method is measured on the publicly available

DRIVE and STARE databases. The average accuracy

0.942 along with true positive rate (TPR) 0.752 and false

positive rate (FPR) 0.041 is very close to the manual seg-

mentation rates obtained by the second observer. The

proposed method is also compared with several state-

of-the-art methods. The proposed method shows higher

average TPR in the same range of FPR and accuracy.

Keywords Retinal image segmentation �Blood vessel detection � Local binary pattern �Adaptive neuro-fuzzy inference system

1 Introduction

The detection and quantitative measurement of variations

in the retinal blood vessels can be used for diagnosis of

several diseases such as diabetic retinopathy, hypertension,

occlusion, glaucoma, obesity, etc. For example, vessel

occlusion makes vessels longer; hypertension reduces

arteries, while diabetes creates new blood vessels. There-

fore, several blood vessel detection methods can be found

in the literature for diagnosis of such diseases [1–7]. Also,

the retinal blood vessel distribution is unique for each

person, and therefore, it could be used for personal iden-

tification [8].

Developments of acquisition equipments enable us to

capture high-resolution images from retina. Therefore,

manual or semiautomatic blood vessel extraction tech-

niques are labor intensive and time consuming, especially

in large database of retinal images. Thus, the develop-

ments of automatic methods for robust blood vessel

extraction are valuable. In the literature, several tech-

niques have been reported for blood vessel segmenta-

tion. These methods generally can be categorized into

three classes: (1) kernel-based, (2) tracking-based, and (3)

classifier-based.

In the kernel-based methods, the retinal images are fil-

tered by various vessel-like kernels. The blood vessel

structures are detected by maximizing the responses of

applied kernels. The mathematical morphology operators

[6, 9] and matched filters [1, 2, 10, 11] are two examples of

this category. In the matched filters, a series of different

Gaussian-shaped filters like simple Gaussian model [1, 2],

dual-Gaussian model [10], or derivative of Gaussian

function [11] are used to detect the blood vessels. How-

ever, the matched filters have strong responses not only to

blood vessels but also to non-vessel edges like bright blobs.

A. Fathi (&) � A. R. Naghsh-Nilchi

Department of Computer Engineering,

The University of Isfahan, Isfahan, Iran

e-mail: [email protected]

A. R. Naghsh-Nilchi

e-mail: [email protected]

123

Neural Comput & Applic (2013) 22 (Suppl 1):S163–S174

DOI 10.1007/s00521-012-1118-8

They also have to use several kernels to detect vessels with

different thickness orientations.

In the tracking-based methods, the vessel seems as a line

and they try to follow vessel edges by exploiting local

information. In these methods, various vessel profile

models such as Gaussian profile [12], generic parametric

model [13], Bayesian probabilistic model [14], and multi-

scale profile [15] are used to find the path that has the best

matches to the vessel profile model. Although these

methods have high performance in detecting blood vessels,

they usually have 2 week spots: the limitation in handling

of bifurcations especially in thin vessels and the needing of

manual seek points.

The classifier-based methods are divided into two sub-

classes: supervised and unsupervised. In the supervised

methods [16–18], some prior information of the labeled

vessels is exploited to decide whether a pixel belongs to

vessel or non-vessel. For this propose, different classifiers

such as artificial neural network [16], Gaussian mixture

model classifier [17], and KNN classifier [18] were used. In

the unsupervised methods, the vessel segmentation is done

without any prior labeling knowledge [19, 20]. In the

classifier-based methods, the performance of detected

vessels heavily depends on the features that are extracted

from retinal images. Various types of feature extraction

methods such as Gabor wavelet transform [17], ridge

detection [18], matched filters [19], and trench detection

[20] were reported in the literature.

Other techniques tried to combine these methods and

improve the performance [21–23]. Mendonca et al. [21]

used morphological operators and region growing algo-

rithm, while Palomera-Perez et al. [22] and Martinez-Perez

et al. [23] employed Hessian-based vesselness and region

growing techniques to extract blood vessels.

In this paper, an efficient and easy-to-implement clas-

sifier-based method is presented for automatically extract-

ing blood vessels. An adaptive neuro-fuzzy inference

system (ANFIS) is used as classifier, and a proper exten-

sion of local binary pattern (LBP) operator is employed to

extract multi-scale statistical and structural features of

blood vessels. The combination of ANFIS and LBP is used

to calculate the vesselness measure of each pixel in retinal

images. A proper and simple procedure is applied in the

postprocessing phase to extract the thin and thick vessels

separately. By applying length filter on the thin and thick

vessels and integrating them, the retinal blood vessel net-

work is detected.

The rest of this paper is organized as follows: a brief

review of adaptive neuro-fuzzy inference system and local

binary patterns is presented in Sects. 2 and 3, respectively.

The proposed method for robust blood vessel detection is

presented in Sect. 4. Experimental results are reported in

Sect. 5. And finally conclusion is given in Sect. 6.

2 Adaptive neuro-fuzzy inference system

The fuzzy logic, proposed by Zadeh [24], not only can be

used as a control methodology but also can be employed as

a data processing tool. Unlike binary logic, which is based

on crisp values of 0 (‘‘false’’) and 1 (‘‘true’’), fuzzy logic

uses a degree of truth by using membership functions,

rules, and fuzzy logic operators. By using of membership

functions, it would be possible to determine the weight of

each input to define final output. The final output will be

obtained using fuzzy ‘‘if–then’’ rules. These rules combine

the various dependencies between input variables using

fuzzy logic operators to describe the final output.

The most critical issue in the fuzzy systems is appro-

priately determining their parameters such as the shape and

location of membership functions and the fuzzy rules

composition. In addition to using trial-and-error method,

one can use learning methods such as artificial neural

network to obtain optimal fuzzy logic parameters from

training data. An adaptive neuro-fuzzy inference system

(ANFIS) was obtained by combination of neural network

and fuzzy inference system [25]. In the ANFIS, either

backpropagation or combination of least square estimate

and backpropagation may be used to estimate membership

function parameters. Although in the fuzzy inference sys-

tem, both premise (if part) and consequence (then part)

parts of fuzzy if–then rules can be fuzzy proposition, in the

ANFIS, the consequence part is a zero- or first-order

polynomial. Such kind of models are called Sugeno-type

fuzzy model [26]. For a first-order Sugeno fuzzy model, a

common rule set with two fuzzy if–then rules is as follows:

Rule 1 : if x is A1 and y is B1 then f1 ¼ p1xþ q1yþ r1

ð1ÞRule 2 : if x is A2 and y is B2 then f2 ¼ p2xþ q2yþ r2

ð2Þ

The corresponding equivalent ANFIS structure and its

reasoning mechanism are shown in Fig. 1. This network

has two kinds of nodes: fixed nodes and adaptive nodes.

The adaptive nodes, which are depicted by rectangles,

contain parameters that may be trained using learning

algorithm, while fixed nodes, which are depicted by circles,

are constant and do not contain any parameters.

In the Fig. 1, the first layer consists of adaptive neurons

used to determine the extent of membership function of

each fuzzy set.

The second layer consists of fixed nodes that simply

perform multiplication operation.

O2;i ¼ wi ¼ lAiðxÞ � lBi

ðyÞ for i ¼ 1; 2 ð3Þ

where O2;i is the output of the ith node in the layer 2 and lS

is an appropriate parameterized membership function for

S164 Neural Comput & Applic (2013) 22 (Suppl 1):S163–S174

123

fuzzy set S. lAiðxÞ and lBi

ðyÞ are the output of the first-

layer nodes that specify the membership value of each

input x or y to its corresponding fuzzy set (Ai for x and Bi

for y), respectively.

In the third layer, constant nodes are used to normalize

the outputs of the previous layers’ nodes as below:

O3;i ¼ �wi ¼ wi

X

k

wk for i ¼ 1; 2

,ð4Þ

The fourth layer consists of adaptive neurons which they

compute a weighted first-order polynomial function as

below:

O4;i ¼ �wifi ¼ �wiðpixþ qiyþ riÞ for i ¼ 1; 2 ð5Þ

where pi, qi, and ri are its parameters obtained during the

training process.

The last layer include a single fixed neuron which it

collects the outputs of the nodes in the previous layer.

Overall output ¼ O5;1 ¼X

i

�wifi ð6Þ

In this paper, a hybrid learning method is used to

estimate the parameters of adaptive neurons. In this type of

learning, in the first step, the parameters of neurons in the

first layer are set to fix random values. Then the parameters

of neurons in the fourth layer are trained with least square

error method. In the next step, these trained parameters are

considered as constants, and the neurons in the first layer

are trained with error backpropagation gradient descent

algorithm. These steps are iterated till the condition of

stopping is satisfied.

3 Local binary pattern operator

Local binary pattern, which was proposed by Ojala et al.

[27, 28], is a very effective multi-resolution statistical

and structural texture primitives descriptor that can be

applied in many applications such as face recognition

[29], fingerprint classification [30], and remote sensing

analysis [31]. In the LBP operator, the primitive patterns

are extracted by comparing the value of P equally spaced

neighborhood points (gi i = 0 to P-1) on the circle (with

radius R) with the value of central pixel (gc). The

primitive patterns are represented with binary codes

(BCP,R):

BCP;RðiÞ ¼1 gi� gc

0 gi\gc

�i ¼ 0; 1; . . .;P� 1 ð7Þ

If the position of each neighborhood point (gi) does not

fall into the center of a pixel, we rounded it to fall into the

center of a nearest pixel. But one can obtain the value of gi

by using the interpolation of the corresponding pixels. The

reason for using the rounding process is to speedup the

calculation of the proposed LBP. In the classical LBP

(LBPriu2) [28], only uniform patterns are selected as local

texture features. The uniform patterns contain at most two

bitwise transitions from 0 to 1 or vise versa in the obtained

binary code (T(BCP,R)) when it is considered as a circular

structure:

LBPriu2P;R ¼

PP�1i¼0 BCP;RðiÞ TðBCP;RÞ� 2

Pþ 1 Otherwise

�ð8Þ

where

TðBCP;RÞ ¼ BCP;RðP� 1Þ � BCP;Rð0Þ��

þXP�2

i¼0

BCP;RðiÞ � BCP;Rðiþ 1Þ�� ð9Þ

The uniformity measure T corresponds to the number of

transitions from 0 to 1 or from 1 to 0 between successive

bits in circular representation of the obtained binary code

(BCP,R). The superscript ‘‘riu2’’ refers to the use of rota-

tion-invariant uniform patterns that have a T value of at

most two. The classical LBP is rotation invariant, because

it assigns a unique label to each pattern based on the

number of its ‘‘1’’ bits, and the placement of ‘‘1’’ bits does

Fig. 1 Two ANFIS fuzzy rules. a ANFIS structure. b Reasoning or membership functions

Neural Comput & Applic (2013) 22 (Suppl 1):S163–S174 S165

123

not have any effect on the LBP outputs. An example of

calculating the LBP value is shown in Fig. 2.

By applying this operator, only uniform pattern such as

flat area, spots, corners, line-ends, and edges, which is

shown in Fig. 3, can be extracted, and all non-uniform

patterns are neglected by integrating them as one pattern

with label P ? 1.

Since in the retinal images, the blood vessel structure is

line pattern with T value greater than 2, the classical

LBPriu2 cannot efficiently describe it. Therefore, we used

an extension of LBP (LBPNE), proposed by authors [32],

which can describe the line patterns efficiently. The for-

mulation of this version of LBP is given in below:

LBPNEP;R ¼

PP�1i¼0 BCP;RðiÞ if TðBCP;RÞ\4

P� 1þPP�1

i¼0 BCP;RðiÞ if TðBCP;RÞ ¼ 4

2P� 5þ TðBCP;RÞ=2 if TðBCP;RÞ[ 4

8<

:

ð10Þ

Since the patterns with line-shaped structures have four

bitwise transitions in their binary code (T(BCP,R) = 4), as

shown in Fig. 4, in this version of LBP, the line patterns are

noticed separately. And also, for the other non-uniform

patterns instead of assigning one label to all of them, we

use one label for each group of them that have same bitwise

transitions (T) value.

By employing different values for P and R, we can

extract multi-resolution patterns as shown in Fig. 4. The

value of R (R [ 0) is referred to radius of circle that P

(P [ 1) equally spaced neighbor points are considered on it

to extract the LBP values. Although each value for P can

be used, the best value for P is equal to the number of

pixels that exists in the perimeter of the corresponding

circle to utilize all vessels’ points for extracting the LBP

values. By detecting multi-resolution patterns in the retinal

images, the efficient feature vectors for blood vessels with

different diameters can be extracted easily.

4 The proposed blood vessel detection method

In this paper, a robust method for automatic blood vessel

extraction is introduced. In the first step, a new and

efficient rotation-invariant LBP operator (LBPNEP;R) with

different values for P and R is applied to extract multi-scale

feature vector for all pixels in the retinal images. Next, the

obtained feature vectors are applied to the trained ANFIS

to indicate the vesselness value of each pixel. Then thin

and thick vessels are separately extracted by applying

simple and proper postprocessing procedures. Finally, the

blood vessel networks are obtained by applying simple

logical OR operation on the detected thin and thick vessels.

The details of these steps are given in the following of this

section. Also the flowchart of the proposed system is

depicted in Fig. 5.

4.1 LBP feature extraction

When the colored images of retinal vessels and their red,

green, and blue channels are visualized separately, as

shown in Fig. 6, the green channel shows the best vessel/

background contrast. Therefore, this channel is selected to

be processed by the LBPNE operators. Since the width of

blood vessels in the retinal images with size of about

700 9 600 pixels is usually in the range of 2 and 10 pixels,

the LBPNE operators in three scales LBPNE18;3, LBPNE

32;5, and

LBPNE48;9are applied to each pixel to cover all vessels’

widths. For other data sets, that their maximum width of

vessels are greater than 10 pixels, these parameters should

be set based on the maximum value of vessels’ width, to

span all vessels’ widths. Another choice is employing a

resizing algorithm to resize the images to about

700 9 605 pixels. The obtained values for these LBP

operators and their corresponding bitwise transition

(T) values are used as multi-scale feature vector:

LBP Feature Vector

¼ LBPNE18;3; TðBC18;3Þ;LBPNE

32;5; TðBC32;5Þ;LBPNE48;9; TðBC48;9Þ

n o

ð11Þ

This feature vector, which can reflect the characteristics

of different vessels, is extracted for all pixels in the retinal

images and then applied to the trained ANFIS (see Sect. 4.2)

to estimate the vesselness values of them.

4.2 Vesselness degree measurement using ANFIS

To estimate the vesselness value of each pixel, an adap-

tive neuro-fuzzy inference system (ANFIS) is employed.

The architecture of the used ANFIS is shown in Fig. 7.

The training data set is directly extracted from the real

retinal images. To this end, we selected five images from

the training set of the DRIVE data set [34]. We ran-

domly selected 100000 vessel and non-vessel points

from the selected training images. For each point, a fea-

ture vector as explained in the Eq. 11 was extracted.

Fig. 2 The details of obtaining the LBP value


123

The output of ANFIS is set in the range of 1 and -1: the

value 1 for vessel and -1 for background. The ANFIS is

trained using a combination of the least squares and the

backpropagation gradient descent method to emulate

training data set. In this type of learning, in the first step,

the parameters of input membership functions (IMF neu-

rons) are set to fix random values. Then the parameters of

output membership functions (OMF neurons) are trained

with least square error method. In the next step, these

trained parameters are considered as constants, and the

neurons in the IMF layer are trained with error backprop-

agation gradient descent algorithm. These steps are iterated

till the condition of stopping is satisfied.

In the test phase, the LBP feature vectors will be

extracted for all pixels in the input retinal images and

applied to the trained ANFIS to indicate the vesselness

degree of them. To reduce the effect of noise, a simple

uniform averaging filter with 5 9 5 kernel structure is

applied to the obtained vesselness values. The results of

this step, shown in Fig. 6e, are used to enhance and detect

thin and thick vessels separately.

4.3 Thin vessel enhancement

To extract thin vessels, the morphological top-hat operator

with suitable circular structuring elements is employed.

Circular structuring elements of radii 2 and 4 are applied to

the obtained vesselness values to highlight the thin vessels

with a specific range of widths. The final thin vessels are

extracted by applying global threshold value, which was

proposed by Otsu [33] to select the threshold value such

that minimizes the intraclass variance in the output binary

images. Since several small regions of non-vessels may be

extracted, and a proper length filter is also applied to

eliminate them. The obtained result of this phase is shown

in Fig. 6f.

4.4 Thick vessel enhancement

The thick vessels are extracted by applying a proper

thresholding process followed by a simple length filtering.

Since, the ratio of blood vessel pixels in the retinal images

is less then 15 %, the threshold value (TV) is adopted such

that its value to be greater than 85 % of existing vesselness

values. For this purpose, we use cumulative density

Fig. 3 The uniform structures

flat area, spot, corner, line-end,

and edge patterns for P = 8.

Dark circle is used to indicate 0

and white to indicate 1

Fig. 4 Multi-resolution line

patterns for (P = 16 and

R = 3), (P = 12 and R = 2),

and (P = 8 and R = 1) from

left to right, respectively. Darkcircle is used to indicate 0 and

white to indicate 1

Fig. 5 The flowchart of the proposed method


123

function (CDF) of the obtained vesselness values to obtain

threshold value as below:

TV ¼ argkfCDF(k) ¼ 0:85g ð12Þ

where k is the quantized vesselness values. After applying

the obtained threshold value, a proper length filter is also

applied to eliminate small regions. The obtained results for

thick vessels are shown in Fig. 6g.

4.5 Label filtering for small region removing

To eliminate small regions, connected component labeling

is used to identify individual objects in the thin and thick

vessel images. Connected component labeling is a simple

image analysis technique that scans an image pixel-

by-pixel and groups its pixels into components based on

pixel connectivity. The label filtering is employed to isolate

the individual objects by using the 4-connected neighborhood

Fig. 6 The obtained results for different steps of the proposed method. a Color image. b Red channel of image. c Green channel of image.

d Blue channel of image. e Smoothed vesselness result. f Detected thin vessels. g Detected thick vessels. h Final detected vessel network

Fig. 7 The architecture of the

trained ANFIS


123

and label propagation. The number of pixels, in the labeled

components, is used as a measure of length feature of

regions. If the area of the region is smaller than a certain

value then that region will be removed. We experimentally

tried different values for eliminating small regions from the

thin and thick vessels, and we found that the best limit

for thin vessels is 60 and 150 and for thick vessels is

300 pixels. These values were obtained for retinal images

with size of about 700 9 600 pixels. The details of these

experiments are given in Sect. 5.2.

4.6 Final vessel network detection

The final blood vessels are obtained by integrating of the

thin and thick networks using logical OR function. The

obtained vessels for final blood vessel network are shown

in Fig. 6h.

5 Experimental results

In the first section of our experiments, the effect of dif-

ferent parameters of the proposed method was evaluated on

images from publicly available DRIVE database [34]. The

DRIVE database consists of 40 images along with manual

segmentation of vessels. It has been divided into training and

test sets, each of which contains 20 images. These images are

captured in digital form using a Canon CR5 3CCD camera at

45� field of view (FOV). The size of images is 565 9

584 pixels and used 8 bits per each color channel.

To evaluate the proposed method, we used detection

accuracy (ACC), true positive rate (TPR), and false posi-

tive rate (FPR) as performance measures. The ACC is

defined as the ratio of the number of correctly classified

pixels to the total number of existing pixels. The TPR is

defined as the ratio of the number of correctly detected

vessel pixels to the total number of vessel pixels that exist

in the ground truth images. The FPR is also defined as the

ratio of the number of non-vessel pixels were classified as

vessels to the total number of non-vessel pixels.

The training of the ANIFS was done using the ima-

ges 1–5 of the training set of the DRIVE database. A

combination of the least-squares method and the back-

propagation gradient descent method for training FIS

membership function parameters is applied to emulate a

given training data set as explained in the Sect. 4.2. The

hand-labeled images by the first expert human were used as

ground truth.

5.1 Experiment on CDF-based thresholding

This experiment was done to evaluate the effect of CDF-

based threshold on the proposed method. This threshold

was employed to vesselness values to extract thick vessels.

We evaluated TPR and FPR of the proposed method when

different values for CDF-based threshold have been used.

We used the images 6–20 from the training set of the

DRIVE database in this experiment. Figure 8 illustrates the

obtained results. From the figure, when the value of K was

reduced from 1 to 0.85, the variation in TPR is more than

FPR; and from 0.85 to 0.7, the value of TPR is fixed, and

only FPR is increased. Therefore, a good trade-off between

the TPR and FPR values is obtain when the CDF is equal to

0.85. Base on this experiment, we used the vesselness value

(k) of this point as threshold value for detection of thick

vessels. Also the ROC curve of the proposed method, when

only the CDF threshold was changed, was extracted from

this figure and shown in Fig. 9 for better understanding of

the effect of CDF threshold.

5.2 Experiment on the size of length filters

To evaluate the effect of length filtering on the proposed

method, we applied different values from 0 to 500 pixels

for thin and thick vessels. We separately calculated the

accuracy (ACC) of thin and thick vessels when different

values for length filtering were used. In this experiment, the

images 6–20 from the training set of the DRIVE database

were used. Figure 10 illustrates the obtained results. From

the figure, the best accuracy for thin vessels with radii 2

and 4 is obtained when the size of length filter is equal to

60 and 150 pixels, respectively. Also the best accuracy for

thick vessels obtains when the size of length filter is equal

to 300 pixels.

Fig. 8 The effect of the CDF thresholding parameter on TPR and

FPR values of the proposed method. The proper value was

emphasized with circle


123

5.3 Experiment on feature vector

To evaluate the benefit of the proposed LBP operator and

selected feature vector, we implemented the proposed

method using different feature vectors obtained by RGB

values, LBPriu2 values, and the proposed LBP (LBPNE)

values. For both LBP operators, not only the obtained LBP

values but also the combination of LBP values and their

transition values (T) were used as feature vector. In these

experiments to train the ANFIS, the images 1–5 from the

training set of the DRIVE database were used, and then all

images in the test set of the DRIVE database were used as

test samples. The obtained results are shown in Table 1.

From the obtained results, it is clear that the using of LBP

operator is superior to RGB value. The highest perfor-

mance rate was achieved when the combination of the

proposed LBP values (LBPNE) and their transition values

(T) were used. It is better in all performance measures and

outperforms 2 % in the TPR greater than the classical LBP

(LBPriu2).

5.4 Comparison with other methods using the DRIVE

database

To emphasize the ability of the proposed method, we

compared it with some state-of-the-art blood detection

methods on all images in the test set of the DRIVE data-

base [34]. For this purpose, the methods proposed by

Chaudhuri et al. [1], Niemeijer et al. [3], Jiang et al. [9],

Zhang et al. [10], Delibasis et al. [13], Soares et al. [17],

Stall et al. [18], Mendonca et al. [21], Palomera-Perez et al.

[22], and Martinez-Perez et al. [23] were used for com-

parison. The results of other methods can be obtained from

the DRIVE database web site [34] or from their original

papers. These results are summarized in Table 2.

The TPR of the proposed method is higher than others,

while its FPR dose not exceed from 3.91 %. Also the

obtained results on the image 16 of the DRIVE database

for the proposed method and some state-of-the-art methods

for better comparison are shown in Fig. 11.

5.5 Comparison with other methods using the STARE

database

The proposed method was also compared on the STARE

database [2] with some state-of-the-art methods. We

selected 20 images from the STARE database that ten of

which contain pathology. These images are captured in

digital form using a TopCon TRV-50 fundus camera at 35�field of view (FOV). The size of images is 700 9

605 pixels and used 8 bits per each color channel. Two

observers manually segmented all images. The perfor-

mance of all methods is compared with first observer as

ground truth. The previous trained ANIFS, which was

trained using DRIVE images, was used again to assess the

robustness of the proposed method. In this experiment, the

methods proposed by Chaudhuri et al. [1], Hoover et al.

[2], Stall et al. [18], Soares et al. [17], Martinez-Perez et al.

[23], Mendonca et al. [21], Palomera-Perez- et al. [22] and

Zhang et al. [10] were used for comparison. The results of

other methods are extracted from their original papers. The

obtained results are presented in Table 3.

In the obtained results, the TPR value of the proposed

method is 75.9 % and higher than others while its

FPR value dose not exceed from 4.4 %. The accuracy

value of the proposed method is similar to the others.

Fig. 9 The obtained ROC curve of the proposed method, on the

images 6–20 of training set of DRIVE database, only by changing

the value of CDF(K) from 1 to 0.7. The true and false positive rates of

the second human observer were indicated with star

Fig. 10 The effect of the length filtering parameter on accuracy

values of the proposed method. The proper values were emphasized

with circles


123

Table 1 The obtained results of

the proposed method for

different setups on the DRIVE

database

Method True positive rate (%) False positive rate (%) Accuracy (%)

ANFIS ? RGB 61.3 7.6 88.4

ANFIS ? LBPriu2 69.4 4.8 92.1

ANFIS ? LBPriu2 ? T 72.1 4.1 93.2

ANFIS ? LBPNE 70.2 4.4 92.7

ANFIS ? LBPNE ? T 74.4 3.9 94.2

Table 2 The obtained vessel

extraction performance of all

methods on the DRIVE

database

Method True positive rate False positive rate Accuracy

Non-expert human 0.7761 0.0275 0.9473

Chaudhuri et al. [1] 0.6168 0.0259 0.9284

Niemeijer et al. [3] 0.6793 0.0199 0.9416

Jiang et al. [9] 0.6478 0.0375 0.9222

Zhang et al. [10] 0.7120 0.0276 0.9382

Delibasis et al. [13] 0.6731 0.0241 0.9377

Soares et al. [17] 0.7283 0.0212 0.9466

Stall et al. [18] 0.7192 0.0227 0.9442

Mendonca et al. [21] 0.7344 0.0236 0.9452

Palomera-Perez- et al. [22] 0.6600 0.0380 0.9220

Martinez-Perez et al. [23] 0.7246 0.0345 0.9344

Proposed method 0.7442 0.0391 0.9418

Fig. 11 The results of different methods on the image 16 from

the database DRIVE. a Original image. b Reference vessel image.

c The result of Chaudhuri et al. [1] method. d The result of Niemeijer

et al. [3] method. e The result of Jiang et al. [9] method. f The result

of Stall et al. [18] method. g The result of Martinez-Perez et al. [23]

method. h The result of the proposed method


123

The obtained results of the proposed method on four

images of the STARE database are also shown in Fig. 12.

Since in this experiment, the test set and training set are

completely independent, the obtained results show the

robustness of the proposed method.

To perform a fair comparison, the TPR values of the

proposed method and some state-of-the-art methods at the

same FPR values on the both DRIVE and STARE

databases are presented in Tables 4 and 5. The methods

proposed by Chaudhuri et al. [1], Hoover et al. [2],

Niemeijer et al. [3], Zana et al. [6], Jiang et al. [9], Zhang

et al. [10], Delibasis et al. [13], Soares et al. [17], Stall et al.

[18], Palomera-Perez- et al. [22], and Martinez-Perez et al.

[23] were used. For each method, the TPR value directly

was extracted from its ROC curve. From these tables, the

proposed method has high TPR values compared to most of

existing methods and competes with the best existing

method on the both DRIVE and STARE databases. Its

average TPR value is greater than 75 %.

Furthermore, the proposed method requires low com-

putational cost and competes with existing fast methods,

see Table 6. Without optimization of its MATLAB code,

Table 3 The obtained vessel

extraction performance of all

methods on the STARE

database

Method True positive rate False positive rate Accuracy

Second observer 0.8949 0.0610 0.9354

Chaudhuri et al. [1] 0.6134 0.0245 0.9384

Hoover et al. [2] 0.6751 0.0433 0.9267

Stall et al. [18] 0.6970 0.0190 0.9516

Soares et al. [17] 0.7165 0.0252 0.9480

Martinez-Perez et al. [23] 0.7506 0.0431 0.9410

Mendonca et al. [21] 0.6996 0.0270 0.9440

Palomera-Perez- et al. [22] 0.7790 0.0551 0.9240

Zhang et al. [10] 0.7177 0.247 0.9484

Proposed method 0.7588 0.0435 0.9414

Fig. 12 The obtained results on four images of the STARE database. Top the original images. Middle the reference images. Bottom the obtained

results of the proposed method


123

it will take about 3.7 min to process one image in the

DRIVE database and 4.3 min to process one image in the

STARE database on a PC with a Pentium-IV 3.2 GHz CPU

and 2.0 GB RAM. These running times are obtained by

averaging the running times of all images of the DRIVE

and STARE databases. In real applications, the computa-

tion time can be significantly reduced by implementing the

algorithm in C/C?? programming.

6 Conclusion

In this paper, we proposed a novel and easy-to-implement

algorithm for automatic blood vessel extraction, which

combines the multi-resolution LBP operator and adaptive

neuro-fuzzy inference system. Since it uses multi-scale

features, which are obtained using LBP, all vessels with

different thicknesses and orientations can be detected

efficiently. In the proposed method, the thin and thick

blood vessels are extracted separately by applying top-hat

transform and simple thresholding as well as length filter-

ing. The final vessels are obtained by combining the thin

and thick vessels using logical OR function.

Experiments on different test images from the DRIVE

and STARE databases are conducted to access the perfor-

mance of the proposed method in comparison with some of

the best state-of-the-art methods. The proposed method is

competitive with or better than other state-of-the-art

methods. On the DRIVE and STARE databases, the TPR

value of the proposed method is 74.4 and 75.9 %, respec-

tively, while its FPR value is 3.9 and 4.3 %, respectively.

The overall accuracy of the proposed method is greater

than 94 %. And also, the running time of the proposed

method competes with existing fast methods. It can process

one image in 3.7 min.

To improve the performance of the proposed method

and reduce its FPR value, we need to use more complex

postprocessing procedure and also use more efficient LBP

operator to extract line, junction, as well as bifurcation

patterns. We will further investigate these aspects in our

future works.

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http://dx.doi.org/10.1016/j.cmpb.2010.03.004

http://www.isi.uu.nl/Research/Databases/DRIVE/results.php

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