1

A New Retinal Vessel Segmentation M e t h o d Using Preprocessed Gabor and Local Binary Patterns

Abstract: A new retinal vascular tissue segmentation algorithm, which utilizes Gabor wavelet and local binary patterns, is introduced. It would be shown that how a simple pre-processing step would increase the accuracy of algorithm. Different features have been proposed for retinal vessel detection. One of the most famous features adapted is Gabor wavelet. Thanks to multi-resolution property of Gabor, combination of scales can be used to extract features. However, similar features in feature vector would increase the inter-correlation and may lead to poor result. Also, Local Binary Pattern (LBP) is applied. LBP is a powerful feature for texture analysis. A wise pre-processing strategy is applied to image with regard to feature extraction technique. Contrary to previous methods where a simple pre-processing scheme applied for all feature extraction methods, here each feature extraction will utilize its own suitable pre- processing. It is showed that this enhances the result of segmentation. The proposed method has a low dimension feature vector having only four features. The pre-processing step enhances the results in comparison to a previous method in term of area under the ROC curve The computational results of simulations show the high performance of the proposed method in term of accuracy and speed. Keywords: Vessel segmentation, Retina, Gabor filter, Local Binary Pattern.

1. Introduction Retinal image analysis has been center of attention for

years. Studying retinal tissues is used in diagnosis of a wide range of diseases such as diabetes, hypertension, arteriosclerosis, cardiovascular disease, stroke and retinopathy of prematurity [1]. In fact, detection of vascular tissues is of great value for physicians. It is also applicable to biometric applications [2]. 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 categories: kernel-based, classifier-based and tracking-based [3]. In kernel-based approaches, 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 [4], [5], [6].

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. [7] and Soares et al. [1] provide two famous classifier-based methods.

Tracking-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, several methods have been proposed [8], [9]. 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 [3]. On the other hand, there were endeavors to enhance results using pre-processing and/or post-processing techniques. Leandro et al. applied morphological operators to enhance the kernel response in a post-processing phase [10].

Wu et al. [4] apply Adaptive Histogram Equalization (AHE) in order to enhance the contrast of retinal images in a pre-processing step. Feng et al. [11] utilize contourlet in preference to AHE. The same approach is adapted by Rezatofighi et al. [12] using an adaptive neuro-fuzzy inference system as classifier.

The aforementioned method enhances overall properties of image. However, some people focused on obtaining enhanced results using wiser techniques. Zhang et al. [13] tried to enhance the response of Gabor kernel by tuning its parameters knowing that image of interest consists of small vessels. Al- Rawi et al. [5] proposed a tuning scheme to improve Gaussian kernel response. However, it is also possible to incorporate both approaches to provide a better segmentation [14].

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 results. However, there are only a few publication feature selection methods in this field.

M. Shahram Moin*, Hamed Rezazadegan Tavakoli**, Ali Broumandnia*** *IEEE Senior Member, Islamic Azad University, Qazvin Branch, moin@qiau.ac.ir

**IEEE Student Member, Islamic Azad University, Science and Research Branch, hrtavakoli@srbiau.ac.ir ***Islamic Azad University, South Tehran Branch, broumandnia@azad.ac.ir

978-1-4244-9708-9/10/$26.00 ©2010 IEEE

2

Examples of such a studies could be find in [7], [15], [16]. Staal et al. applied a sequential forward feature selection scheme [7]. Lupascu et al. studied different greedy heuristics in order to select the best possible feature set [15]. Tavakoli et al. applied a hierarchical feature selection scheme to select the best feature set [16].

It is also possible to focus both on enhancement and classification to improve segmentation results, e.g. Farzin et al. applied both pre-processing and post processing as well as a wise feature matching for classification [2].

In this paper, we apply a wise pre-processing method based on feature extraction characteristic. Fig. 1 shows the proposed mechanism for vessel detection. It is showed that the proposed method increases the area under the ROC. In this paper, also a speed assessment of methods is done which shows the Introduced method is a fast technique.

Fig. 1. Vessel Detection Framework

The rest of this paper is organized as follows. In

section 2, pre-processing, feature extraction, and selection are studied, (including Gabor kernel and Local Binary Pattern). In section3, classification part of method is discussed. It is followed by Experiments in section 4. Section 5 is the conclusion.

2. Features In this section feature extraction procedure is

explained. At first examining Gabor and Local Binary Pattern (LBP) feature extraction methods is studied. As, our pre-processing is feature dependant it will be presented after feature extraction. Finally, feature selection is studied. 2.1 Feature Extraction

2.1.1 Gabor Feature Extraction Gabor kernel is a linear filter whose impulse response

is defined by a harmonic function multiplied by a Gaussian function. Thus, there exist 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, the Soares et al. [1] approach was adapted in order to extract Gabor features. The two-dimensional Gabor wavelet is defined as

G (x) = exp( xkj 0 ) exp( 2

21 Ax ) (1)

where 1j 1] , 1,[, 2/1diagA is a 2 × 2 matrix defining the angular distance in any desired direction, and k0 is the frequency.

The continues wavelet transform of an image, Tψ (b, θ, 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:

2/1),,( CabT a )exp( jkb ψ (a k )f(k) 2kd (2)

where b is displacement vector, θ is direction, a is scale, Cψ denotes normalizing constant, f defines Fourier transform of image, and ψ is the Fourier transform of wavelet’s complex conjugate.

Setting = 4 and k0 = [0, 3], for each pixel the maximum wavelet r e sp o n se over θ sp a n n i n g from 0 to170 was computed. The same procedure was repeated for different scales. Fig. 2 shows maximum response for different Gabor wavelets.

(a) (b)

(c) (d)

Fig. 2. Maximum Gabor response over 0◦ to 170◦ for different scales, (a) a = 2, (b) a = 3, (c) a = 4, (d) a = 5.

2.1.2 LBP Feature Extraction The LBP operator was first introduced as a

complementary measure for local image contrast [17]. It is a fast and easy to compute operator. LBP is a powerful means of texture analysis [18]. The Original operator

3

calculates the central pixel value of a 3×3-neighborhood by summing up the thresholded values of neighbourhood weighted by powers of two.

Ojala et al. [18] extend the operator to use different neighbourhoods. The extended operator is defined by Equation (3).

LBPP,R (x, y) =1

0

P

n

s(f (x, y) − f (xn , yn ))2n . (3)

where,

0 , 00 , 1

xx

xs and P represents the number of sampling pixels, R is the radius of neighbourhood, and f (x, y) denotes pixel (x, y) of image f .

The operator can easily be adapted to be rotation invariant by a bitwise right shift. It is denoted by ri

RPLBP , . This property can be further improved by finer quantization of angular space using uniform patterns [18]. A uniform pattern is a pattern that has at most two 0/1 transitions in the pattern. A uniform rotation invariant pattern is defined by 2

,riu

RPLBP . As LBP is rotation and gradient invariant and

computationally efficient, it has became the center of attention in recent years. LBP is used in many diverse applications such as face analysis [19], [20], paper characterization [21], wood inspection [17] and texture analysis [18].

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 2

1,8riuLBP , 2

2,8riuLBP

and 22,16

riuLBP . Fig. 3 represents LBP response.

Fig3. Sample LBP responses, (a) 2

1,8riuLBP , (b) 2

2,8riuLBP ,(c) 2

2,16riuLBP .

2.2 Pre-processing Each feature extraction has its own characteristic. In

fact, the nature of input image affects the result of feature extraction. Here, based on this fact, we propose

different pre-processing steps for each of our feature extraction techniques.

It can be construed from Fig. 3 that LBP is a fine feature extraction technique. In fact, it is so sensitive to small change of luminance and contrast in an image. So a small artifact can bias the feature extraction greatly. In order to lessen this effect, the input image is decomposed using Symlets wavelets into different levels and the approximation of each level is selected as the input to LBP feature extraction. Because, we are using the approximation part of wavelet, it is as if we smoothed the input image prior to extracting features.

Contrary to LBP, Gabor is a coarse feature extraction utility. It is quiet evident by comparing Fig. 2 and Fig. 3. In fact, its response over a smoothed image would not be as good as expected. So, an adaptive contrast enhancement [22] is applied over the image before calculating Gabor response.

In fact, as shown in Fig. 1 each feature extraction has its own suitable pre-processing block. 2.3 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. [7] applied the sequential scheme provided in [23] to select proper features. Lupascu [15] utilized five different feature selection heuristics, in order to find the best heuristic and feature sets. In this paper we applied method of [16] to select the features. It is a hierarchical scheme based on maximizing relevance and minimizing redundancy [24].

3. Classification A supervised classification method, i. e. Gaussian

Mixture Model (GMM) classifier is used. GMM is a parametric method in which likelihood is defined by linear combination of Gaussian functions [25].

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.

. 2

);2()2()1()1( 1

otherwiseCDecide

CPCxpCPCxpifCDecide (4)

4. Experiments

We tested proposed method on the DRIVE1 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 benchmark.

The inverted green channel of image was taken to be processed. Gabor response was generated for a = 2, 3,

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

4

4, 5. The feature vector consisted of four Gabor responses, 2

1,8riuLBP , 2

2,8riuLBP , 2

2,16riuLBP and Inverted

Green Channel(IGC). 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 (k =10 for vessels and k = 10 for non-vessels, k is the number of Gaussians).

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

In order to assess the features to be selected, we applied accuracy (ACC). It is calculated by dividing the number of correct classified samples by number of total samples. Accuracy of each feature was measured individually which is reported in table I. As shown, Gabor with scale 3 is the dominant feature.

The selected feature vector consists of inverted green

channel, Gabor (a = 2, 3) and 21,8

riuLBP . In order to show the superiority of this feature set, a comparison is made. Table II shows this.

In order to pre-process the image for LBP feature extraction, we decomposed the image using Symlet family (Sym-4). In order to find the best level of decomposition we tested different levels of decomposition. Fig. 4 shows the ROC curve obtained by this experiment. As shown second level of decomposition is superior to the others.

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. The ROC curves are provided in Fig. 6

Considering the number of features, the proposed method uses only 4 features, achieving accuracy of 0.9447, which is acceptable in comparison to Staal et al. [7] with 18 features and accuracy of 0.9442. It means the two methods have almost identical performance. In fact, our method’s strength is that it only uses 4 features. It is also better than method of [16] in terms of accuracy and Az .

Some segmentation examples are shown in Fig. 5. Obviously, it is really hard to distinguish between Soares et al. [1] segmentation and that of ours.

To summarize, the main advantage of proposed method is that it has almost identical performance with

Staal et al. [7], using only four features rather eighteen features. Also, in comparison to Soares et al. [1], it is faster (Table. IV).

TABLEII AC C U R AC Y E VA L UAT I O N O F S E L E C T E D F E AT U R E S . ACC I S

AC C U R AC Y, TPR I S T RU E P O S I T I V E R E S P O N S E A N D FPR I S FA L S E POSITIVE RESPONSE TH E Y A R E C A L C U L AT E D AT T H R E S H O L D L E V EL

O F lt 0.5.

5. Conclusion In this paper, we presented application of Gabor and

local binary pattern analysis for retinal vessel segmentation. We also presented the idea of feature-extraction-dependant image pre- processing.

As shown in the experiments, performance achieved is acceptable considering the accuracy of similar methods.

Proposed method is also faster in feature extraction phase. Although the method provides acceptable results, there are other aspects that we did not mentioned in this work. Our future work’s focus would be on classification where a promising result is expected.

5

Fig. 5. Segmentation results using different features. (a) Benchmark, (b) Segmentation using proposed method, (c) Segmentation using Soares et al. [1], (d) Segmentation using Tavakoli et al. [16].

6

References [1] J. V. B. Soares, J. J. G. Leandro, R. M. C. Jr., H. F.

Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification,” IEEE Transactions On Medical Imaging, vol. 25, no. 9, 2006.

[2] H. Farzin, H. Abrishami-Moghaddam, and M.-S. Moin, “A novel retinal identification system,” EURASIP Journal on Advances in Signal Pro cessing, vol. 2008, p. 10.

[3] K. Vermeer, F. Vos, H. Lemij, and A. Vossepoel, “A model based method for retinal blood vessel detection.” Computers in Biology and Medicine, vol. 34, no. 3, 2004.

[4] D. Wu, M. Zhang, v. Liu, and W. Bauman, “On the adaptive detection of blood vessels in retinal images,” IEEE Transactions On Biomedical Engineering, vol. 53, no. 2, 2006.

[5] M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med., vol. 37, no. 2, pp. 262–267, 2007.

[6] H. Zhu, H. Shu, and L. Luo, “Blood vessels segmentation in retina via wavelet transforms using steerable filters,” in Computer-Based Medical Systems, 2004. CBMS 2004. Proceedings. 17th IEEE Symposium on, 2004, pp. 316– 321.

[7] J. Staal, M. D. Abrmoff, M. Niemeijer, M. A. Viergever, and B. v. Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions On Medical Imaging, vol. 23, no. 4, 2004.

[8] P. D. Axel Pinz Stefan Bernogger and A. Kruger, “Mapping the human retina,” IEEE Transactions On Medical Imaging, vol. 17, no. 4, 1998.

[9] T. Henderson and G. Choikim, “Segmentation of vessels in retinal images by shortest path histogramming,” in Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on, 2003, pp. 685– 688.

[10] J. Leandro, R. Cesar, Jr., and H. Jelinek, “Blood vessels segmentation in retina: preliminary assessment of themathematical morphology and of the wavelet transform techniques,” in Computer Graphics and Image Processing, 2001 Proceedings of XIV Brazilian Symposium on, Floria- nopolis, Brazil, 2001, pp. 84–90.

[11] P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the contourlet transform,” Pattern Recogn. Lett., vol. 28, no. 4, pp. 516–522, 2007.

[12] S. H. Rezatofighi, A. Roodaki, and H. Ahmadi Noubari, “An enhanced segmentation of blood vessels in retinal images using contourlet,” in 30th Annual International IEEE EMBS Conference, 2008.

[13] M. Zhang, D. Wu, and J.-C. Liu, “On the small vessel detection in high resolution retinal images,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, Shanghai, 2005, pp.

3177–3179. [14] H. R. Tavakoli and H. R. Pourreza, “An enhanced retinal

vessel detection algorithm,” in Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering. Springer Netherlands, 2008.

[15] C. A. Lupascu, D. Tegolo, and E. Trucco, “A comparative study on feature selection for retinal vessel segmentation using fabc,” in CAIP’09: Proceedings of the 13th International Conference on ComputerAnalysis of Images and Patterns. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 655–662.

[16] H. R. Tavakoli, H. R. Pourreza, and S. R. Quchani, “Study of gabor and local bianry patterns for retinal image analysis,” in International Workshop on Advanced Computational Intelligence. Shuzhu, China: IEEE, Sept 2010 (accepted).

[17] T. Maenpaa, “The local binary pattern approach to texture analysis extensions and applications,” Ph.D. dissertation, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, 2003.

[18] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002.

[19] A. Hadid and M. Pietikainen, “Combining appearance and motion for face and gender recognition from videos.” Pattern Recognition, 42(11):2818-2827, 2009.

[22] G. Zhao and M. Pietikainen, “Boosted multi-resolution spatiotemporaldescriptors for facial expression recognition.” Pattern Recognition Let- ters 30(12):1117-1127, 2009.

[21] M. Turtinen, M. Pietikainen, and O. Silven, “Visual characterization of paper using isomap and local binary patterns.” 2006, iEICE Transactions on Information and Systems E89D(7):2076-2083.

[22] J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Transactions On Image Processing, vol. 9, no. 5, 2000.

[23] A. Whitney, “A direct method of non parametric measurement selection,”IEEE Trans. Comput., vol. C-20, p. 11001103, 1971.

[24] H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1226–1238, 2005.

[25] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classificaiton. NewYork: Wiley, 2001.