Automatic Detection of Optic disc in Eye Fundus Images- A REVIEW

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1 Automatic Detection of Optic disc in Eye Fundus Images- A REVIEW Manpreet Kaur 1 & Mandeep Kaur 2 M. tech Student, Department of Electronics and Communication Engineering, Punjabi University Punjabi university Patiala, Punjab, India manpreet89sidhu@gmail.com Assistant Professor, Department of Electronics and Communication Engineering, Punjabi University Punjabi university Patiala, Punjab, India mani9sidhu@gmail.com Abstract - In the automatic analysis of retinal images for diagnosing various eye diseases such as diabetic retinopathy, glaucoma etc. the automatic detection of the optic disc is a crucial step. Before processing the fundus image for detecting any type of retinal disease it is necessary to mask out the anatomical features of eye so that they will not be mistaken as abnormality. Optic disc often considered as a bright lesion if not segmented out and results in false positives that affect the final results, therefore it is crucial to detect the optic disc. In this paper, a review of various methods for localizing and detecting optic disc along with their merits and demerits is presented. Keywords Fundus Images, Optic Disc, Automated Detection, Diabetic Retinopathy, Glaucoma. I. INTRODUCTION Optic Disc is commonly used to describe the portion of the optic nerve that is clinically visible on examination. Optic Disk is a small spot on the retinal surface of eye and is located about 3 mm to the nasal side of the macula. It is the point from where the major blood vessels enter into the eye for retinal supply. Optic Disk represents the beginning of the optic nerve and is the only part of the retina which is unresponsive to light [1]. Fundus photography is used to document the anatomical parts of retina such as optic disc, fovea, blood vessels as shown in fig.1 and abnormalities related to diabetic retinopathy (damage to the retina from diabetes), glaucoma etc. This is because retinal details may be easier to visualize in stereoscopic fundus photographs as opposed to with direct examination. Early diagnosis is very important to control rapid increase in the number of instances of diabetic retinopathy. Fig 1. Fundus image showing anatomical features of eye. Optic disc localization and detection is required as a precondition for later stages in many methods applied for the detection of various pathological structures in retinal images. The WORLD HEALTH ORGANIZATION (WHO) has examined that Diabetic retinopathy is on the priority list of eye conditions which can be fairly prevented and treated. It is recognised that eye care services for diabetic patients be integrated into strategic VISION 2020 national plans. So there is an urgent of automatic system that can detect features like optic disc, blood vessels and fovea in the retinal images which further help the ophthalmologists in early detection of disease like Diabetic retinopathy and Glaucoma which can further cause blindness. The potential solution is to develop an algorithm that can automatically detect optic disc at early stage so that Available online: Page 1002

2 ophthalmologist could easily detect the abnormalities rather than distinguishing anatomical features from pathological features in the fundus image. The speedy growth of information processing system and the emergence of inexpensive ophthalmic imaging devises have led to the development of automated techniques for detection of optic disc. This paper provides review of various automated techniques along with their strength and weakness. In this review we discuss various algorithms on automated detection of optic disc based on digital image analysis. II. LITERATURE SURVEY In 2006, Michael D. Abramoff et al. [2] presented a method using KNN regression for detection of the position of optic disc. The proposed method starts by preparing a regression model of the position of optic disc. All vessel pixels are searched for those which are lying in the optic disc according to the regression model using prior vessel segmentation. Then output of regression is blurred to handle noise. The final point which is chosen is closest to the middle of the optic disc. This optic disc location regression is a remarkable approach for fast optic disc location in fundus colour images that is helpful in early diagnosis of any type of eye disease. Then, Aliaa Abdel-Haleim et al.(2008) [3] proposed a method in which initially, a binary mask is generated and then the method uses illumination equalization which starts by normalizing luminosity and then contrast is normalized throughout the image using adaptive histogram equalization methods. The author purposes the use of simple matched filter to match the directional pattern of the retinal blood vessels at the OD vicinity. A simple and standard 2-D Gaussian matched filter is used to segment the retinal vessels. To represent the OD-centre candidates, the segmented vessels are thinned and filtered using local intensity. S. Sekhar et al. (2008) [4] proposed a novel method to localise both the optic disc and fovea automatically. The morphological operations and the Hough transform are used to localize the optic disc. The optic disk localisation is improved by a robust shade correction operator and the automatic thresholding. The author proposes that the identification of OD can also be improved by adjusting the Hough transform that can identify both circular and elliptical shapes properly. The results obtained by this novel method are promising and exhibit both applicability and superior performance of the proposed method compared to existing methods. Then, in 2009, Arturo Aquino et al. [5] have proposed a technique in which the author has used the detection procedure that comprises two independent methodologies. On one hand, image contrast analysis and structure filtering techniques is used to obtain a pixel that belongs to the OD and, on the other hand, by applying mathematical morphology, edge detection techniques and the Circular Hough Transform a circular approximation of the OD boundary is estimated. The proposed method provides reliability, robustness and efficiency. Daniel Welfer et al. (2009) [6] propose a new adaptive method using mathematical morphology for the automatic detection of the optic disk. The proposed method not only detects the optic disk, but also detects the optic disk contour (i.e. boundaries) and it is also robust under varying illumination conditions. In order to reduce the vessels influence in the optic disk location, this method does not require a vessels elimination stage. On the other side, the proposed approach requires to undergo some specific preprocessing stages (e.g. enhancement and smoothing steps to detect the vascular tree). Then, in 2010, M. Usman Akram et al. proposed an automated system using average filter and thresholding for optic disk localization and Hough Transform for optic disc In this method, average filter is used for preprocessing of retinal images and ROI (region of interest) is extracted prior to optic disc Localization of Optic disc is done by using average masking and histogram and Hough transform is used to detect the optic disc. Then, Siddalingaswamy P. C. et al. (2010) presented a new technique that used Iterative thresholding method followed by connected component analysis for the automatic localization and boundary detection of the optic disc. The proposed method is used to locate the approximate centre of the optic disc. Then to find the correct boundary of the optic disc, a geometric model based implicit active contour model is applied. The results obtained by this method states that the geometric based implicit active contour models provide a better segmentation for images that contain weak boundaries when compared to other parametric models. Shijian Lu et al. (2010) presented an automatic optic disc (OD) detection technique. In the proposed method firstly a retinal background surface is estimated through an iterative Savitzky-Golay smoothing procedure which is capable of compensating the irregular illumination and other types of imaging artefacts such as haze from the given retinal image. Then the global thresholding of the difference between the retinal image and the estimated background surface is used to detect the OD. Finally, a pair of morphological postprocessing operations is applied to determine an OD boundary. Then, in 2011, Shijian Lu et al. proposed a technique in which the unique circular bright structure linked with the OD is used to mark the location of optic disc. The OD has a circular shape and is brighter as compared to surrounding pixels whose intensity gradually decreases with their increasing distances from the OD centre. To capture such circular brightness structure, a line operator is designed which can estimates the variation in image brightness along multiple line segments of specific orientations. In order to locate the OD accurately, the Available online: Page 1003

3 positioning of the line segment with the minimum/maximum variation that has specific pattern are used. The advantage of the proposed method is that different types of retinal lesion and imaging artefacts cannot affect its performance. In 2012, H. Yu et al. present a new, fast, and fully automatic Optic disc localization and segmentation algorithm. In this algorithm, firstly template matching is used to identify OD location. The template is made adaptive based on the estimated OD radius and different image resolutions. Then, to determine the location of OD, vessel characteristics on the OD are used. Then a fast, hybrid level set model is applied to the segmentation of the disk boundary which makes use of both region and local gradient information. To remove blood vessels and bright regions other than the OD that affect segmentation morphological filtering is used. Then, Hung-Kuei Hsiao et al. (2012), presented his work in which a novel illumination correction Operation is used for optic disc localization, and a supervised gradient vector flow snake (SGVF snake) model is used to complete contour segmentation. The proposed illumination correction operator solves the problem of non-uniform illumination by providing significant contrast between the optic disc and background through the image. The vessel occlusion and fuzzy disc boundaries are the factors that render the use of Conventional GVF snake to segment contour. Due to this reason, the SGVF snake is drawn-out in each time of deformation iteration in order to classify and update contour points according to their corresponding feature information. From the training images, the feature vector extraction and the statistical information are generated that is used for classification. Sandra Morales et al. (2012) proposed a method based on a variant of the watershed transformation, the stochastic watershed, for drawing out the optic disc contour. In this method, principal component analysis (PCA) and a previous pre-processing, based on mathematical morphology, are performed prior to segmentation of the image. The PCA is used is to obtain the grey-scale image so that it is able to maximize the separation between the different objects of the image. The watershed transformation is used to locate the region of interest (ROI). Then to eliminate false contours, which are detected due generally to the blood vessels that pass through the OD, morphological closing was performed. Jihene Malek et al. (2012), proposed a method to locate and detect boundary of the optic disk. Two methodologies are presented by the author for automatic OD detection: one to locate the OD using iterative thresholding method following principal component analysis (PCA) and another one to segment its boundary by applying region-based active contour model in a variational level set formulation (RSF). To solve the boundary leakage problem the author uses an improved geometric active contour model. The results obtained by the proposed method were compared with conventional method using a geometric active contour models (ACM) and then verified with hand-drawn ground truth that indicate 89 % accuracy for identification and % average accuracy in localizing the optic disc boundary. Then, Daniel Welfer et al. (2013), proposed a method that uses adaptive morphological method to detect the optic disk centre and the optic disk rim. In this method, author tries to detect the optic disk centre and the optic disk rim, without assuming any pre-defined shape (e.g. a circle of a predefined size). Furthermore, this method is not affected by the outgoing vessels passing through the optic disk, and it has been designed to detect optic disk features even in the presence of diabetic lesions such as exudates, neovascularization and microhemorrhages, or illumination artefacts in the retinal image. K.Padmanaban et al. (2013) presented a technique in which Fuzzy C Means clustering (FCM) is used for the localization of the optic disc in eye fundus image. The author makes the use of green plane from the RGB fundus image because it provides better contrast as compared to red and blue planes. Initially the optic disc is identified by the brightest points which are called Region of Interest (ROI) because the optic disc has brighter intensity than other region. Before the FCM technique is applied to cluster the optic disc, median filtering technique is applied to the initial ROI for de-noising. In 2014, Niluthpol Chowdhury Mithun et al. presented an algorithm to automatically detect retinal features of fundus image, such as optic disc and blood vessel. The author proposes the use of blue plane to detect the optic disc and blood vessel pixels. Then, the vessel pixels are connected by using OD location. The detection method makes use of basic operations like edge detection, binary thresholding and morphological operation. The results show that the proposed method provides efficient results even when illumination and image size is varying. Then, Diego Marin et al. (2014), proposed a methodology that can enhance the bright region on the intensity channel of the retinal image by performing iterative opening closing morphological operations. Then, to obtain a reduced region of interest, where the centre and the OD pixel region a 2-step automatic thresholding procedure is applied that takes into account the blood vessel convergence at the OD. Final results are obtained by applying the circular Hough transform on a set of OD boundary candidates generated by the application of the Prewitt edge detector. Marwan D. Saleh et al. (2014) proposed a method that consist of three major stages, namely optic disc localization, preprocessing and segmentation. Firstly, fast Fourier transform based on template matching is applied to obtain a seed point located on the optic disc. Then, this seed point is used as an input to the region growing technique for the purpose of segmentation. The major contributions of this work are that it exploits the use of the FFT-based template matching Available online: Page 1004

4 for localization of OD, then it uses the combination of standard deviation and Otsu s threshold for calculating automated threshold value and it provides fast computational time. TABLE I COMPARISON OF VARIOUS METHODS Sr.No. Reference Method used Results Advantages/ Disadvantages 1. Michael D. Abramoff et al. KNN regression model is used. 99.9% Advantage is that it is fast (30s) and easier to optimize. 2. Aliaa Abdel-Haleim et al. Direction matched filter is used for OD 98.7% Advantage is its ability to obtain the vessels direction map (VDM) implicitly while segmentation, without any additional algorithm 3. S. Sekhar et al. Morphological operations and Hough transform is used for OD localisation. 4. Daniel Welfer et al. Adaptive morphological approach is used for OD segmentation. 5. M. Usman Akram et al. Average filtering and thresholding then Hough transform is applied for OD 6. Siddalingaswamy P. C. et al. Geometric model based implicit active contour Model is applied to detect OD. 7. Shijian Lu et al. An iterative Savitzky-Golay smoothing procedure is used for OD 8. H. Yu et al. Direction matched filtering and level sets are used for OD 9. Hung-Kuei Hsiao et al. A supervised gradient vector flow snake (SGVF Snake) model is used for OD 10. Sandra Morales et al. A variant of watershed transformation, the stochastic watershed along with PCA is used. 11. Jihene Malek et al. Region-based active Contour model in a variational level set formulation (RSF) is used to detect OD. 12. Daniel Welfer et al. Two-stage morphological approach is used for OD 13. K.Padmanaban et al. Fuzzy C Mean (FCM) is used for OD 14. Niluthpol Chowdhury Mithun et al. Edge detection, binary thresholding and morphological Operators are used for OD 15. Marwan D. Saleh et al. FFT-based template matching is used for localization of OD. OD* - optic disc. *- data not provided. 94.4% 97.75% 96.7% 90.67±5% and 94.06±5% specificity 84.37% Disadvantage is that it is not robust and not appropriate for clinical purpose, further improvement is needed. Advantage of this method is that it is robust to the different imaging conditions. Tested over four datasets and is highly reliable. Advantage of the method it that it is highly optimized due to iterative threshold method. Advantage is that the method requires no prior knowledge of the retinal blood vessels and hence simpler & more efficient for implementation. 99% Advantage of this method is that it is very fast and robust that provides high accuracy in results. 91% Use of illumination correction operator for enhancing contrast is the biggest advantage of this method % 90.33% and 99.7% specificity. 91.3% 91.27% & 99.81% specificity. Advantage is its full automation and less computational time. Advantage of this technique is that it is not influenced by the outgoing vessels crossing the optic disk, and illumination artefacts are present in the retinal image. Highly efficient method. Advantage of the method is that it is robust to variation of illumination and image size. Available online: Page 1005

5 III. CONCLUSION This review paper analyses the merits and demerits of the existing automated techniques for localization and detection of optic disc. Automatic detection of optic disc presents many of the challenges. The blood vessels emerging out of the optic disc render the proper detection of it. So there is a need of an effective automated detection method so that optic disc can be detected prior to the detection of any abnormality. In this paper, some existing methods are reviewed to give a complete view of the field. On the basis of this work, the researchers can get an idea about automated optic disc detection and can develop more effective and better method for detection of optic disc that can be masked out at subsequent stages while detecting diabetic retinopathy, glaucoma etc. REFERENCES [1] Xiayu Xu, Simultaneous automatic detection of optic disc and fovea, MS (Master of Science) thesis, University of Iowa, [2] Michael D. 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Eswaran and Junaidi Abdullah, Automated Segmentation of Optic Disc in Fundus Images, Proceedings Of IEEE 10th International Colloquium On Signal Processing & Its Applications (CSPA), Available online: Page 1006