Optic Disc Detection Using Geometric Properties and ...

Optic Disc Detection Using Geometric Properties and GVF snake Shantala Giraddi

Dr Jagadeesh Pujari

Dr P.S.Hiremath

Dept of Computer Science and Engineering B V Bhoomraddi College of Engineering and Technology Hubli, India [email protected]

Dept of Information Science and Engineering S.D.M College of Engineering and Technology Hubli, India [email protected]

Dept of Computer Applications (MCA) B V B College of Engineering and Technology Hubli, India [email protected]

changes in shape and depth. These changes occur over many years in a slow manner. The optic disc images over the time are compared; the changes indicate a measure of disease progression. Not only boundary of the optic disc can be detected more precisely but other clinical information can be gained using image processing techniques. Various algorithms based on active contour models, gradient vector field ( GVF), Hough transform have been developed for the segmentation of OD. There is a large variation of intensity within OD due to interference of dark blood vessels. Sinthanayothin et al.[1] located the optic disc using the maximum standard deviation of intensity, but OD boundary was not detected. Mendels [2] proposed a technique for identification of OD boundary in the presence of glaucoma using GVF snakes, which eliminates the need for accurate initialization of the contour. The study was conducted using a small dataset of nine images. Walter and Klein [3] used the morphological operations for optic disc localization and then implemented the watershed transformation for detection of contours of the optic disc. Lalonde [4] designed a method in which potential OD areas are located using pyramidal decomposition and OD contour is detected using Hausdorff Distance, for which a priori knowledge about location of OD in retinal image is used. It is observed that OD found is away from the true OD center and also Hausdorff distance for finding the OD contour fails in case of very diffused boundary. The method proposed by Hoover and Goldbaum [5] performed optic nerve detection by using the convergence property of the blood vessel as the primary indicator along with the brightness of the nerve as a secondary attribute. Chutatape and Li[6] used principal component analysis (PCA) to differentiate the optic disc region from other main object regions. Modified active contour model (ACM) was proposed for optic disc contour detection. Niemeijer [7] built K-NN regression models using 100 training images to find the relation between distance to the optic disc center, and a feature vector measured around a circular template. Various features like mean, standard deviation of the vessel width were used, that yielded a good result of 99.90 detection rate. Park et al. [8] performed iterative thresholding to find the brightest region, later circularity of the region usedto detect optic disc, and finally segmented the optic disc by applying the Hough

Abstract— The optic disc (OD) segmentation in an retinal image is prerequisite for an computerized detection of diabetic retinopathy and also for monitoring changes due to diseases such as glaucoma. The OD segmentation is also used for the detection of other anatomical structures like fovea and vascular tree. Many algorithms based on thresholding, active contour model, GVF snake and clustering have been proposed for the segmentation of OD. In this study, a novel method is proposed for optic disc segmentation. The method makes use of P-Tile thresholding for detecting patch of OD. Connected component analysis is performed for eliminating false positives. This step yields initial patch of optic disc for which centroid correction is performed. GVF snake model is used for finding the contour of OD. The method is robust and effective even in the low contrast images as well as in the presence of other pathological structures like exudates. The experimentation has been done using benchmark retinal image databases, namely, diaretdb0, diaretdb1, DRIVE. The results show accuracy of 98% with diaretdb0, 97% with diaretdb1 and 100% with DRIVE . Keywords— Retinal image, Optic disc, thresholding, centroid correction, histogram.

I. INTRODUCTION Retinal image screening and analysis is done for the detection of diabetic retinopathy. It is also performed for the detection of macular degeneration, and glaucoma. The retinal images obtained by fundus camera are regularly used by ophthalmologists for retinal examination. The retinal vasculature, optic disc (OD) and macula are the important anatomical structures in the retina. The optic disc is the bright area slightly elliptical in nature. The blood vessels congregate at OD. The changes that occur in OD in terms of shape, color or depth of OD are indicators of different ophthalmic deceases. Severity of certain deceases, such as glaucoma and diabetic retinopathies can be measured with the change in the dimensions of OD. Figure 1.a shows the typical retinal fundus image. Optic disc segmentation is an important prerequisite for computerized detection of diabetic retinopathy. The optic disc has same color characteristics as that of exudates. In automated diabetic retinopathy, OD is segmented and removed from further analysis in order to avoid false positives. The Figure 1.b shows optic disc along with exudates in a diabetic retinopathy image. The progression of glaucoma makes optic nerve fibers degenerate and optic disc exhibits

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transform. Perez-Rovira and Trucco [9][10] presented an idea of detecting three main anatomical structures macula, optic disc and main arcade. A prior knowledge is used to predict the best OD location, which yielded a result of 94.2% on STARE database. Sekar et.al [12] localized optic disc by using mathematical morphological operations, followed by placing an ROI which is three times bigger the initial blob. The exact contour of optic disc is found by using Hough transform on the gradient image in the ROI. Siddalingaswamy and Gopalakrishna [13] proposed a two step process for the OD detection. In the first step, approximate center of the OD is found by iterative thresholding then connected component analysis is performed . In the next step, implicit active contour is used to perform segmentation. Qureshi et al.[15] attempts a combination of various optic disc and macula detection algorithms and used combined voting system so as to improve the accuracy. The proposed method is based on pyramidal decomposition, edge detection, entropy filter and Hough transformation. Intensity of the pixels in OD region is higher and this fact is used to separate it from the rest of the image using suitable intensity based thresholding techniques. However there may be other brighter regions which may resemble OD and therefore the intensity based techniques may yield more than one location of OD from a typical fundus image. Fuzzy convergence method is also time consuming and not free of error. So, the localization and boundary determination of OD is an important task and it is still a challenging task. The objective of this work .is to propose a method for localization optic disc using adaptive thresholding and geometrical properties. The accurate segmentation of OD is performed using GVF snake model. The proposed method is robust enough to segment OD even in poor contrast images and also in images having other abnormalities like exudates. Figure 1.a shows retinal image having poorly contrasted OD and 1.b shows image having pathologies. Database: Three different databases are used for the study. The Diaretdb0 and Diaretdb1 are well known publicly available databases. The number of images in each of the databases is given in the Table 1. The images from Diaretdb0 and Diaretdb1 are of size 1500X1152 in png format. The DRIVE database contains 40 color retinal images.

II. PROPOSED METHODOLOGY The block diagram of the proposed methodology is shown in Figure 2, which employs adaptive thresholding for segmentation of OD followed by the connected component analysis to eliminate false positives. Since this process may or may not yield complete OD, the centroid correction is made to find the actual center of OD. Finally, to perform accurate OD boundary detection, the GVF snake model is used. TABLE.I. Retinal image databases considered for the study

[1] [2] [3]

Database Diaretdb0 Diaretdb1 DRIVE

Number of Images 130 89 40

Figure 2. Schematic diagram of proposed methodology

A. Preprocessing The retinal images are resized to 575X750 to maintain uniformity. Green channel in each image is separated, since the contrast is high in green channel. The contrast is enhanced by applying contrast-limited adaptive histogram equalization (CLAHE). B. Selection of Optimal Thresholding The OD has highest intensity in a fundus image. In a healthy image, top 5% brightest pixels of retinal image belong to OD [1]. The databases Diaretdb0, Diaretdb1, DRIVE and own dataset consist of the images of varying contrast. Sometimes OD may be of lower intensity as shown in the Figure1.a and sometimes there may be pathologies brighter

Figure 1.a. Retinal image with poor contrast b. Retinal Image with presence of Exudates

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than OD. Therefore, selection of threshold value for OD segmentation is a challenging task. When the object is brighter/darker than background and it occupies a known percentile 1/P of image, we can use P-Tile method for thresholding. The diameter of OD is around 80-100 pixels [13] and we can estimate the its size in terms of pixels. The number of pixels in the OD can be approximately given by:

C. Geometric properties of OD for the removal of false positives: The retinal image may have abnormalities like exudates which have same color and brightness characteristics as that of optic disc and a presence of crescent (due to improper illumination or improper focusing). In these cases, the P-Tile algorithm may yield other regions also along with OD region. figure 3.b shows the presence of other regions which are false positives. The geometric properties area, eccentricity are used for the removal of false positives.

Figure 3.a shows contrast enhanced image. Figure 3.b shows the image after thresholding using P-Tile method.

1. Area Connected component analysis of thresholded image is performed to remove too small and too large regions. Having calculated the area of optic disc DiscArea, the regions having area less than 10% and more than 12% of DiscArea are eliminated using an area opening operation. Figure 3.c shows the images after elimination of these regions. 2. Measure of Eccentricity The eccentricity of ellipse is the ratio of length of minor axis and its length of major axis. The value is between 0 and 1. Eccentricity of a circle is 0 and that of a straight line is 1. It is given by the Eq.(2).

a. Contrast enhanced retinal images

OD is slightly oval with a circularity of 1.1 with mean vertical diameters of 1.88 and horizontal disc diameters of 1.77 mm [15] and eccentricity of OD=0.11. Because of uneven illumination, the thresholding process never yields complete OD. The region having lowest eccentricity is assumed to be candidate OD. All the regions with Eccentricity>0.7 are eliminated. Figure 3.d shows further elimination of false positive regions based on eccentricity.

b. Segmentation by P-Tile thresholding

3. Centroid Correction Although there are many illumination correction algorithms, illumination of retinal images cannot be corrected beyond certain level [19]. As seen in Figure 4.b, 4c and 4.d shows how unwanted regions are segmented by reducing the threshold for the image shown in Figure 4.a. Figure 5 shows portion of optic disc detected by using adaptive thresholding method and actual OD center. For the accurate detection of OD boundary, gradient vector flow (GVF) snake is used, which is initialized with the centroid of the candidate OD. Since thresholding process has yielded only a patch OD, its centroid needs to be corrected before placing the initial curve of GVF. Since the images obtained for diabetic retinopathy (DR) are ETDRS protocol compliant, macula appears in the center of the image and OD appears either on right side or left side of image. [19]. 129 out of 130 images in Diaretdb0 are ETDRS compliant. Before the curve is initialized for boundary detection by ACM, we need to perform some centroid correction as described below:

c. Elimination of too small and too large regions

d. Elimination of false positives by eccentricity measure Figure 3. Segmentation followed by elimination of false positive OD regions

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If OD is on left side of image, then centroid needs to be Shifted left and the required correction is:

Centroid of OD

Actual centroid

If OD is on right side of image, then centroid needs to be shifted right and the required correction is:

Figure 5. Left retinal image with a patch of OD detected on the right side of OD.

D.OD detection using GVF The OD boundary detection is performed using gradient vector flow (GVF) snakes. After the centroid correction.GVF snake, proposed by Xu [21], is applied to overcome the drawbacks of parametric active contour. Geometric active contours are based on gradient vector field (GVF) that makes use of an external force field calculated as a diffusion of gradient vectors of the edge maps derived from the image. This enhances the ability of the curve to evolve into boundary concavities and thus OD boundary can be fitted more accurately [22][23]. Figure 6 shows some sample experimental results of OD contour detection in retinal images by the proposed method.

a. Retinal image

c. segmentation by educing the threshold by 0.1

Since localization of OD is accurate and OD radius is around 45-50 pixels, initial curve radius is small with 70 pixels. The number of iterations is set to be 200. Thirty images selected from Diaretdb0 database for evaluation of segmentation. OD contour marked expert is compared with that detected by proposed GVF snake method. Figure 7 .a OD marked by expert and 7.b shows OD contour determined by proposed method. Both these are binarised as shown in figure 7.c, 7.d and overlapped to measure sensitivity and specificity.

b.Segmentation by P-Tile

d. Segmentation by reducing the threshold by 0.2 Figure 6. Sample retinal images with OD detected.

Figure 4. Segmentation by reducing threshold

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TABLE III. Sensitivity and specificity of OD segmentation by GVF snake

III. EXPERIMENTAL RESULTS The proposed method is experimented with three benchmark datasets, namely, diaretdb01, diaretdb02 and DRIVE. For OD localization, results of both the proposed method and the methods proposed in the literature is presented in the Table 2. It is evident that the proposed method has yielded better results than other methods.

(a) OD marked by experts

Number of Images

Average Sensitivity

30

93%

Average Specificity 95%

IV. CONCLUSION Authors proposed and developed a system for OD localization using P-Tile segmentation and geometric properties like eccentricity and area. OD segmentation is performed using GVF snake. Since the database has lot of variation in terms of color and contrast, authors would suggest geometric properties are better in localizing the OD. The method has yielded promising result of 98.49% even in the presence of various degrees of diabetic retinopathy. The poor quality of images can be attributed to the lower accuracy for own dataset from vasan Eye care. Even though there are many algorithms for enhancing contrast, each one of them has its own limitation. Authors would suggest quality analysis of images as future scope since poor quality images would incorrectly identify the OD as well as anomalies like exudates.

(b) OD boundary by GVF curve

ACKNOWLEDGMENT

© Thresholded image of (a)

The authors are grateful to Dr Rushikesh Naigoankar, Optholmologist, Vasan Eyecare, Hubli, India. for providing retinal images. Authors would like to acknowledge Dr Sneha Hegde, Optholmologist, Karnataka Institute of Medical Sciences, Hubli, India, for providing the domain knowledge and rendering the manual segmentation of the retinal images.

(d)Thresholded image of (b)

REFERENCES

Figure 7. OD detection by the proposed method and OD marked by an expert [1]

For OD segmentation, the following parameters are used for the active contour model α=0.02, 0.6 kappa=0.05, wl=0.2; we=4; wt=0.2; iterations = 200; Gaussian function σr = 0.1. These parameters are determined empirically using five images Overall sensitivity of 93% and specificity of 95% is obtained for boundary extraction (Table 3).

[2]

[3]

TABLE II. The results of OD localization by the proposed method with the methods reported in the literature (Considering only standard databases) Methodology Diaretdb0 Diaretdb1 DRIVE Average Number of 130 89 40 images ODpd [4] 89.52% 88.99% 80.55% 86.35(%) ODed [4] 77.56% 75.46% 97.22% 83.41(%) ODfv[7] 77.56% 75.46% 97.22% 83.41(%) ODef[16] 95.29% 93,7% 98.61% 95.86(%) ODht[17] 80.12% 76.41% 86.10% 80.87(%) ODdg[18] 98.46% 96.62% 100% 98.36% Proposed 97.69% 97.75% 100% 98.49% method

[4]

[5]

[6]

[7]

ODpd – By pyramidal decomposition ODed - By edge detection ODfv - By feature vector and uniform sample grid ODef - By entrophy filter ODht - By Hough transform ODdg - By geometric properties

[8]

[9]

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