Third International Workshop on Advanced Computational Intelligence August 25-27,2010 - Suzhou, Jiangsu, China

Study of Gabor and Local Binary Patterns for Retinal Image Analysis

Hamed Rezazadegan Tavakoli, Hamid-Reza Pourreza and Saeed Rahati Quchani

Abstract- In this paper selection of proper feature for retinal vascular tissue segmentation is studied. Different features have been proposed for retinal vessel detection. One of the most famous features adapted is Gabor wavelet. Due to multi resolution property of Gabor, combination of scales can be used to extract features. However, similar features in feature vector would increase the possibility of inter-correlation and not an apt result would be achieved.

Also, local binary pattern (LBP) is studied. LBP is a powerful feature for texture analysis. Although LBP itself is not as good as Gabor for vessel detection , it will be showed enhances the result of segmentation.

In order to select the best feature vector, a hierarchical feature selection method is proposed. At the fist step a set of candid features is selected which later reduces to the set of best possible features. It is shown that the combination of some resolutions of local binary pattern and Gabor, including the inverted green channel would strncture the best feature vector.

I. INT RODUCTION

RETINAL image analysis has been center of attention for years. Studying retinal tissues makes diagnosis of a

wide range of diseases such as diabetes, hypertension, arte­riosclerosis, cardiovascular disease, stroke and retinopathy of prematurity possible [1]. In fact, detection of vascular tissues is of great value for physicians.

In recent decades, the advent of the computer as a medical diagnosis assistant made computer assisted vessel detection popular. Several methods have already been developed for retinal vessel detection. We can categorize these methods into three [2]: kernel-based, classifier-based and tracking-based. In kernel-based approach, a kernel response is calculated over image. Afterward, a segmentation technique is applied to separate vascular and non-vascular tissues. Matched filter based methods are exemplar of these techniques, e.g. [3], [4], [5].

Classifier-based methods utilize a classifier over a large number of features. Feature extraction is often done using one or two kernel from the kernel-based approaches. Staal et al. [6] and Soares et al. [1] provide two famous classifier­based methods.

Hamed Rezazadegan Tavakoli is a PhD student at Islamic Azad university, Science and Research Branch, Tehran, Iran . He is also a member of Young Researchers' Club, Islamic Azad university, Mashhad Branch, Mashhad, Iran (email: hrtavakoli@srbiau.ac.ir).

Hamid-Reza Pourreza is with Department of Computer Engineering, Faculty of Engineering, Ferdwosi university of Mashhad, Mashhad, Iran (email: hpourreza@um.ac.ir).

Saeed Rahati Quchani is with Department of Electrical Engineering, Fac­ulty of Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran (email: rahati@mshdiau.ac.ir).

978-1-4244-6337-4/10/$26.00 @2010 IEEE

Tracker-based approaches use a model to follow the vessel. They mostly start from one or more points and keep track of vessels by a model, e.g. [7], [8]. However, these methods are prune to ambiguity due to branches and junctions.

Many tried to improve the performance of vessel detection by combining techniques from the above categories [2]. On the other hand, there were endeavors to enhance results using pre-processing and/or post-processing techniques. Leandro et al. [9] applied morphological operators to enhance the kernel response in a post-processing phase.

Wu et al. [3] apply adaptive histogram equalization (AHE) in order to enhance the contrast of retinal images in a pre­processing step. Feng et al. [10] utilize contourlet in prefer­ence to AHE . The same approach is adapted by Rezatofighi et al. [11] using an adaptive neuro-fuzzy inference system as classifier.

The aforementioned methods enhances overall properties of image. However, Some people focused on obtaining enhanced results using wiser techniques. Zhang et al. [12] tried to enhance the response of Gabor kernel by tuning its parameters knowing that image of interest consists of small vessels. AI-Rawi et al. [4] proposed a tuning scheme to improve Gaussian kernel response. However, it is also possible to incorporate both approaches to provide a better segmentation [13].

In case of classifier-based approaches one aspect that can help enhancement of segmentation is feature selection, as judicious selection of features will improve classification result. However, there are few publication feature selection in this field. Examples of such a studies could be find in [6], [14]. Staal et al. [6] applied a sequential forward feature selection scheme. Lupa§cu et al. [14] studied different greedy heuristics in order to select the best possible feature set.

In this paper we examine different Gabor kernel reso­lutions' role as feature vector. We extend our study by including different resolutions of local binary pattern and inverted green channel. A hierarchical feature selection is proposed. At the fist step a set of candid features is selected which later reduces to the set of best possible features. Later, a Gaussian Mixture Model was used for segmenting tissues into vessels and none-vessels.

II. FE AT URES

In this section feature extraction procedure is explained. After examining Gabor and local binary pattern (LBP) feature extraction methods, feature selection is studied.

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A. Feature Extraction

1) Gabor Feature Extraction: Gabor kernel is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. So, there exists different definitions for Gabor kernel in the literature.

There is a direct relationship between Gabor kernel and Gabor wavelet. In fact, it is possible to study an image both in spatial space and wavelet space. However, we preferred the latter. So, an approach the same as Soares et al. [1] was adapted in order to extract Gabor features. The two­dimensional Gabor wavelet is defined as

. 1 'I/lc(x) = eXP(Jkox)exP(-"2IAxI2) (1)

where j = ;=T, A = diag[c1/2,1],€ � 1 is a 2 x 2 defining the angular distance in any desired direction. ko is the frequency.

The continues wavelet transform, T'If;(b, e, a), is defined in terms of scalar product of input image f with the transformed wavelet. It can be implemented easily using fast fourier transform as follows:

T'If;(b,e,a) = C:;1/2a J exp(jkb){j;*(a_(jk)j(k)d� (2)

where b is displacement vector, e is direction, a is scale, C'If; denotes normalizing constant, j defines fourier transform of image, and {j;* is the fourier transform of wavelet's complex conjugate.

Setting € = 4 and ko = [0,3], for each pixel the maximum wavelet response over e spanning from 0° to 170° was computed. The same procedure was repeated for different scales. Fig. 1 shows maximum response for different Gabor responses.

2) LBP Feature Extraction: The LBP operator was first introduced as a complementary measure for local image contrast [15]. It is a fast and easy to compute operator. LBP is a powerful means of texture analysis [16]. The Original operator calculates the central pixel value of a 3 x 3-neighborhood by summing up the thresholded values of neighborhood weighted by powers of two.

Ojala et al. [16] extend the operator to use different neighborhoods. The extended operator is defined by (3), where P represents the number of sampling pixels, R is the radius of neighborhood, and f(x, y) denotes pixel (x, y) of image f.

LBPp,R(X, y) = L�':� s(f(x, y) - f(xn, Yn))2n.

s(x) = {I , x � 0

o , x < 0

(3)

The operator can easily be adapted to be rotation invariant by a bitwise right shift. It is denoted by LBPpiR. This property can be further improved by finer quanti�ation of angular space using uniform patterns [16]. A uniform pattern

(a) (b)

(c) (d) Fig. I. Maximum Gabor response over 0° to 1700 for different scales, (a) a = 2, (b) a = 3, (c) a = 4, (d) a = 5.

is a pattern that has at most two OIl transitions in the pattern. A uniform rotation invariant pattern is defined by LBPpi Yl.

As LBP is rotation and gradient invariant, and comp�ta­tional efficient it has became the center of attention in recent years. LBP is used in many diverse applications such as face analysis [17], [18], paper characterization [19], wood inspection [15] and texture analysis [16].

Due to blood vessel properties a rotation invariant feature extractor is required to detect vessels. Hence, Rotation invari­ant LBP is selected for vessel segmentation. In this paper, we utilize LBpsS·2, LBPs,�u2 and LBP[j':t Fig. 2 represents LBP response.

B. Feature Selection

Feature selection is referred to identifying the most char­acterizing features of observed data. In many applications this results in classification error reduction. In fact, it is wise to analyze the extracted features before providing them to the classifier.

In case of retinal vessel segmentation, Staal et al. [6] applied the sequential scheme provided in [20] to select proper features. It starts with an empty set and adds the best feature that satisfies some criteria in each step. Finally, the set with the best performance is chosen. Depending on the database used, Staal et al. [6] successfully reduce the number of feature from twenty seven to eighteen for Utrecht database and thirteen for Hoover database.

Lupa§cu [14] utilized five different feature selection heuristics, in order to find the best heuristic and feature sets. The first two heuristics are correlation based feature selection (CFS) with different search strategies.

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(c) Fig. 2. Sample LBP response, (a) LBPs,\,,2, (b) LBPs,

i2,,2, (c) LBP[J;l.

The third heuristic is a consistency based feature selection, where inconsistent features (feature with the same value but different labels) are reduced. At first the data is sorted based on the number of inconsistencies. Later they apply a CFS on the sorted data.

The fourth heuristic is entropy based. knowing that fea­tures with smaller entropy are more discriminant, the CFS is applied to entropy sorted data.

The last heuristic utilizes the MIT correlation [21] scheme as the feature merit in CFS approach. We use HI. H2, H3, H4, H5 to refer to each heuristic, respectively.

1) Proposed Feature Selection Scheme: In this paper we present a heuristic based on Peng et al. [22] approach. Their method has two stage. At the first phase the candid feature set is selected maximizing maximum relevance (4) and minimum redundancy (5). This is achieved by optimizing condition (6), which is referred to as "minimal-redundancy­maximal-relevance" (mRMR).

max D(S, c), 1 D = jSf L [(Xi; c) xiES

(4)

minR(S), (5)

(6)

where S is the feature set, Xk is kth feature, c is the target class, [ has the form of (7) for multivariate densities, p.

11 p(Sm,C) [(Sm, C) = p(Sm, c) log p(Sm)P(c) dSmdc (7)

We modified this step. Instead of applying the fist step on all the features of feature vector, we apply it on feature class subsets. The subsets consists of the features generated with the same feature extractor. The reason behind this is that features from the same feature extractor, e.g. Gabor different scales, is expected to contain more redundant data. In fact, the mRMR would help us to eliminate redundant data while making sure maximizing relevance. The algorithm is as follows:

1) Partition feature vector into i groups, where i is the number of feature extraction methods used.

2) Repeat the following procedure for each feature group, Gj.

• Sort the features using mRMR (6) to select n feature sets.

• Compare all the feature sets to find the range,n, within which the classification error is small.

• Whitin n, find the smallest classification error. The optimal size of candidate feature set is chosen the smallest number of features that minimize error.

3) Combine the feature sets to achieve the final feature set, S = Ui Gj.

Afterwards, a compact feature subset is achieved by com­bining the feature selector and a classifier as described in [22].

III. CL ASSIFICATION

A supervised classification is adapted. A Gaussian mixture model (GMM) classifer is used. GMM is a Bayesian clas­sifier in which likelihood is defined by liner combination of Gaussian functions [23].

In Bayesian classification the Bayes decision rule is ap­plied for decision making after training the classifier. It states that class Ci is winner if and only if multiplication of likelihood and prior probability of Ci is dominant, i.e.

{ Decide C1 Decide C2 otherwise.

IV. EXPERIMENT S

(8)

We tested proposed method on the DRIVEl database. It consists of training and test sets, each containing twenty images. The database has manually segmented and labeled images that can be used as ground truth.

The inverted green channel of image was taken to be pro­cessed. Gabor response was generated for a = 2,3,4,5. The feature vector consisted of four Gabor responses, LB Pg,i1U2 , LBPg,i.;2, LBP[j';i and inverted green channel (IGC).

lean be downloaded at http://www.isLuu.nl/Research. Databases/DRIVE/download.php

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A normal transformation is applied to each image's fea­ture, and one million samples were selected as training data. The classifier was trained using twenty Gaussian kernels for vessels and non-vessels. Performance analysis in feature selection was done using accuracy of segmentation (ACC). It is calculated dividing the number of correct classified samples by number of total samples.

We used an Intel 2.4GHz PC with 1 GB of memory. The code implemented and ran using Matlab R2008a, on a windows XP.

Accuracy of each feature was measured individually which is reported in table I. As shown, Gabor with scale 3 is the dominant feature.

TABLE I

ACCURACY EVALUATION OF INDIVIDUAL FEATURE.

Feature ACC Gabor a - 2 0.9344 Gabor a = 3 0.9364 Gabor a = 4 0.9278 Gabor a = 5 0.9159

LBp'rui2 0.8707 8,1 LBp'rui2 0.8707 8,2 LBprui2 0.8707 16,2

IGC 0.8803

We conducted two experiments, one using the modified feature selection and one using feature selection described in [22]. We also tested all the features to produce evaluation. We use f 81. f 82, f 83 respectively to refer to each feature set. We also provided true positive response (TPR) and false positive response (FPR). Table II shows evaluation result for each feature set.

As previous experiment shows feature set selected using the proposed method (f 81) has the highest accuracy, lowest false positive response, and true positive response. This requires us to provide a ROC curve analysis in order to understand difference of feature selection methods better.

Fig. 3 shows result of ROC curve analysis for three feature sets. As it is obvious f 81 has lower FPR. That's why the accuracy is higher. On the other hand, one may argue that considering table II, f 82 has a higher TPR. We should say that those results are produced using strict threshold of 0.5 which can be enhanced for each feature set. In other words, by selecting a careful threshold for f 81 we would be able to produce a higher TPR. Selecting a careful threshold for both f 81 and f 82, it is evident that f 81 would gain better accuracy due to lower FPR.

From aforementioned facts, It can be derived that the proposed feature selection method outperforms that of Peng et al. [22]. The main difference between these two methods is that we are simply incorporating the expert information by grouping features.

Finally, we provide a comparative study of different retinal vessel detection methods by providing the accuracy and area under the ROC (Az) of different published methods. Table III provides this information.

s:: o 'B

0.8

£ 0.6 <I) >

'';:: . (Ii 8.. 0.4 <I) ::l !::

0.2

:.-" ,.. . '1Y; . .. . . . ..

--- feature set I

- - - feature set 2

feature set 3

o�----�------�------�----�------� o 0.2 0.4 0.6 0.8

false positive fraction

Fig. 3. ROC curve analysis. Feature set 1 (f 81) is produced using proposed feature selection method. Feature set 2 (f82) is produced using method in [22]. Feature set 3 (f 83) consists of all the features.

TABLE III

COMPARATIVE S TUDY.

Method Number of Features

Proposed method 4 Soares et al. [1] 5

Staal et aI. [6] 18 HI 7 H2 4 H3 21

Lupa�cu et al. [14] H

4 20

H5 16

ACC

0.9444 0.9466 0.9442 0.9554 0.9551 0.9536 0.9532 0.9572

0.9497 0.9614 0.9520 0.9482 0.9393 0.8970 0.8953 0.9544

Considering the number of features the proposed method uses only 4 features, achieving accuracy of 0.9444, which is acceptable in comparison to Staal et al. [6] which utilize 18 features achieving 0.9442. Also the Az difference for these two methods is 0.0023 which is not a great leap. It means the two methods have almost identical performance. In fact, our method's strength is that it only uses 4 features.

The other method which uses four features is [14] using H 2. Comparing these two methods we find out that accuracy of our method is not as good as this one. However, Az of our method is 0.0104 better than that of [14].

Although we provided some quantitative measure, it is hard to have a comprehensive understanding without ob­serving some qualitative results. In fact, some segmentation examples using different techniques is provided in fig. 4.

As shown in fig. 4, it is really hard to distinguish between Soares et al. [1] segmentation and that of ours. The accuracy of the two methods only differ 0.0022 in favor of [1]. Also Az reported is better than that of our method. However, again the FPR seems to be in favor of our method. This is also evident from fig. 5.

The main advantage of proposed method is that it has almost identical performance with Staal et al. [6] only using four features rather eighteen features.

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TABLE II

ACCURACY EVALUATION OF SELEC TED FEATURES. ACC IS ACCURACY, TPR IS TRUE POSITIVE RESPONSE AND FPR IS FALSE POSITIVE RESPONSE

Feature Set

lSI = {IGC· Gabor a = 2 3· LBp'rui2} , " 8,1 I S2 = {Gabor a = 2 3 5· LBprui2} , " 16,2 I S3 = All features

Selection Method

proposed method Peng et al. [22]

(a)

(b)

(c)

(d)

ACC TPR FPR

0.9444 0.7047 0.0197 0.9391 0.7196 0.0279 0.9373 0.7590 0.0460

Fig. 4. Segmentation results using different features. (a) Ground-truth, (b) Segmentation using selected features by proposed method, (c) Segmentation using Soares et al. [Il features, (d) Segmentation using all of the features.

531

c o '';::; u

0.8

� 0.6 (1) >

:� --- Proposed method

- . - Soares et al. � 0.4 a. (1) :J ....

.....

0.2 ......... ' ........ .

OL-----�----�------�----�----� o 0.2 0.4 0.6 0.8

false positive fraction

Fig. 5. ROC curve analysis for proposed method and Soares et al.

V. CONCLUSION

In this paper, we presented a feature selection scheme for retinal image analysis. The method works on the basis of finding the best feature subset of best feature subsets.

At first we selected the best features of one category, e.g. LBP features. Later, we find the best subset from the union of selected best category features' subsets.

As shown in the experiments performance achieved us­ing the four selected features is acceptable considering the accuracy of similar methods.

We also used LBP feature extraction for retinal images. It was shown that it enhances the overall result, having lower FPR.

Our focus in this work was enhancing the final result by selecting a proper feature. However, there are other aspects that we did not mentioned in this work. It is expected that further performance enhancement would be achieved by incorporating preprocessing and post processing techniques.

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