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1 Localization of Optic Disc in Retinal Fundus Images for Glaucoma and Diabetes Chaitali D. Dhumane*1, Prof. S. B. Patil*2 *1(Student of Electronics & Telecommunication Department, Sinhgad College of Engineering, Pune, India) *2(Asst. Professor of Electronics & Telecommunication Department, Sinhgad College of Engineering, Pune) Abstract : Optic disc is the brightest region in the retinal fundus images. It is the optic nerve head through which visual information is transferred to the brain. Localization of optic disc in retinal images is important marker for analysis of different retinal diseases. Current techniques for localization include morphological operations, clustering method, pyramidal decomposition and thresholding. This paper proposes pixel count based method for localization of optic disc. In this technique, number of pixels in disc is calculated. Then threshold is applied by using histogram and pixel count. After localizing disc area using this threshold, centre of the disc is marked. Disc centre is important landmark for analysis of various retinal diseases. The proposed method has b een evaluated on standard databases DRIONS-DB (110 images) and DRIVE (40 images). This technique is able to locate optic disc and its centre in 92.66% cases. This method can be used to localize optic disc in analysis of glaucoma and diabetic retinopathy. Keywords Diabetic retinopathy, glaucoma, optic disc, retinal fundus images. INTRODUCTION Optic disc is the important landmark for analysis of retinal eye diseases such as diabetic retinopathy, glaucoma etc. Fig.1 shows the fundus image of retina showing optic disc. It is the brightest part on the retina. Blood vessels and optic nerves enter the eye through optic disc. Optic nerves carry visual information and send it to brain. The brightest region at the centre of the disc is called as optic cup. Optic disc is also called as optic nerve head. There exhibit remarkable changes in the optic disc due to retinal diseases. Fig.1. Retinal fundus image with optic disc. Brightest part inside disc is cup. Thus to detect the disease, many times optic disc is examined. Manual examination of the retinal images is time consuming. Errors may be included as it is manual. Therefore computer aided detection is u sed. Localization of o ptic disc is important landmark for automated computer aided 1

2 detection systems. In diabetic retinopathy, there may be br ight regions present on the retina other than optic disc. These regions may be mistaken as disc if proper detection is not implemented. Therefore, correct localization of disc is essential for further processing of image to detect various diseases. This paper proposes pixel count based thresholding [1] technique to locate the disc. Threshold value is decided using pixel count and histogram. STATE OF THE ART There are various techniques proposed for localization of disc. Sekhar et al al [2] applied morphological operations on the retinal image to localize disc. In this me thod, blood vessels are removed using morphological operations. Hough transform is used to get disc boundary. This Color retinal fundus image shows better contrast in method is applied on the DRIVE and STARE databases. This method cannot find centre of the disc. Bealieu et al [3] proposed disc localization images are first converted to corresponding using pyramidal decomposition and Hausdorff based template matching method. This method is applied on 40 i mages (local database). Position found by pyramid approach is sometimes far from true optic disc centre. Hausdorff approach fails on images where optic disc contour is much diffused. Sharma et al [4] uses k-means clustering algorithm for localizing optic disc. This method is tested on DRIVE database. It doesn t find centre of disc. This paper proposes pixel count based thresholding method to localize the optic disc. Threshold value is decided using pixel count and histogram. First, color retinal image is converted to suitable channel image which gives better results in corresponding databases. Number of pixels occupied by disc is calculated and histogram of image is plotted. Threshold value is evaluated using pixel count of disc and histogram. Then centre of disc is marked. PROPOSED WORK This paper proposes localization of the optic disc by thresholding method. In this method, first pixel count of optic disc is calculated. For this purpose, diameter of r etina is found out. Then using pixel count and histogram of image, threshold is calculated. When this threshold is applied to image, one or more clusters identified. Then using morphological operations, region of interest is extracted. Centre of d isc is found out by centroid method. A. Finding Diameter of Retina red channel for DRIONS-DB [5] database and in blue channel for DRIVE [6] database. Therefore channels. To calculate number of pixels in optic disc, diameter of d isc is required. Ostu s thresholding is applied to image to get binary one. To remove noise present outside retina, morphological opening operation is used. This is because such noise can cause error in calculating diameter. Difference between rightmost white pixel and leftmost white pixel is diameter of retina. B. Counting Optic Disc Pixels Optic disc size varies person to person. It is slightly oval in shape. Its vert ical diameter is greater than horizontal one by 9%. The mean vertical and horizontal diameters are 1.88 and 1.77 mm respectively. Number of pixels occupied by optic disc is nothing but the area of disc in terms of pixels. It is given by the formula. 2

3 = ( ). ( ) D is diam eter of retina in terms of pixe ls. When compared with retina diameter, it is f ound that vertical disc diameter is 1/8 and horizontal disc diameter is 1/7.33 of the retina diameter. C. Finding Threshold Histogram of image is examined at each intensity level from highest level to lowest level. When number of pixels is greater than pixel count and histogram of intensity i is greater than the next one by 10% then that intensity value i is taken as threshold value. This process is repeated until condition is not satisfied. This can be mathematically modeled as follows. For i=256 to 1; add=0 add add H i i 1 H x i i i 1 H i H i If P 1 & & x i 0. 1 Then End add count T i T is threshold value. this threshold value when applied to image, some images give more than one clusters. To remove unwanted clusters, morphological operations are used. D. Morphological Operations applied which fills black portion in white area with white pixels. To eliminate unnecessary clusters, opening operation is used. This function works exactly opposite of c losing operation. Structuring element used in both operations is disk shaped of size 10. After this, if there are still clusters present other than disc then areaopen operation is used. Centre of disc plays important role in cup detection for analysis of glaucoma. Disc centre can be find by using properties of region of interest. Centroid of the region of interest is found out which is disc centre. A. Data Sets RESULTS AND DISCUSSION This method is implemented on DRIONS-DB (110 images) and DRIVE (40 images). DRIONS-DB contains images of glaucomatous retina. Among 110 images, 106 images show desirable results and localizes optic disc cor rectly. DRIVE is diabetic retinopathy database. It contains 40 images out of which 33 images localize disc correctly. B. Diameter of Retina To eliminate unwanted bright areas, morphological operations [7] are used. After applying threshold, the disc obtained gives some discontinuities due to veins present in retina. Thus closing operation is Fig. 2. Diameter of retina original image red channel image binary i mage bou ndary of retina to calculate diameter. 3

4 Diameter of r etina is required to calculate pixel count. Fig. 2 shows steps for getting diameter of retina. The image is from DRIONS-DB database. database. 33 images out of 40 show desirable results in this database. C. Applying Threshold Threshold is applied to the image and resultant image has multiple clusters. (e) (f) Fig.3. Thresholding Original image from DRIONS-DB database Thresholded image of Original image from DRIVE database Thresholded image of. Fig. 3 shows thresholded image both in DRIONS- DB database and DRIVE database. These multiple (g) (h) clusters are removed by m orphological operations to get required cluster. Then centre of disc is marked. Fig. 4 sh ows optic disc after morphological operations. This removes all unwanted regions and gives disc region as a one single blob. Centre of the disc is marked. Table I shows results on different databases. DRIONS-DB database is glaucoma database. Number of successful cases in this database is 106 out of 110. DRIVE is diabetes Fig.4. Final region of interest and optic disc centre Image from DRIONS-DB database Thresholded image of Image after morphological operations on Centre of the optic disc (e)image from DRIVE database (f)thresholded image of (e) (g)image after morphological operations on (f) (h)centre of the disc. Table I Results on Different Databases 4

5 Dataset Type Total Images Successful cases success rate DRIONS Glaucoma DB DRIVE Diabetic retinopathy CONCLUSION This paper proposes an pixel based thresholding method for localization of disc. This is an adaptive method in which pixel count for each image is calculated automatically. This method gives 96.36% result on g laucoma database (DRIONS- DB) and % result on diabetic retinopathy (DRIVE) database. Localization of optic disc is important marker for analyzing various retina diseases. This result of localization of disc can be used for further processing on disc for detecting glaucoma from cup to disc ratio. Segmenting disc gives boundary of disc and cup. Acknowledgements We would like to thank the DRIONS-DB ( DB.html) for providing glaucoma database, image science institute for pr oviding DRIVE database ( and SCOE, Pune for providing platform. REFERENCES Journal Papers [1] Godse, Dr. D. S. B ormane, Automated Localization of Optic Disc in Retinal Images, International Journal of Advanced Computer Science and Applications, (IJACSA), Vol. 4, No. 2, [2] S.Sekhar, W.Al-Nuaimy and A.K.Nandi, Automated Localisation of O ptic Disk and Fovea in Retinal Fundus Images, 16th European Signal Processing Conference (EUSIPCO), August 25-29, [3] Marc Lalonde, Mario Beaulieu, and Langis Gagnon, Fast and Robust Optic Disc Detection Using Pyramidal Decomposition and Hausdorff Based Template Matching, IEEE transactions on medical imaging, vol. 20, no. 11, November [4] Arushi Sharma, Nita Nimabrte, Sapana Dhanvijay, Localization Of Optic Disc In Retinal Images By Using An Efficient K-Means Clustering Algorithm, IRF International Conference, February 2014 [5] E.J. Carmona, M. Rincón, J. García-Feijoo and J. M. Martínez-de-la-Casa, Identification of the optic nerve head with g enetic algorithms. Artificial Intelligence in Medicine, 2008, Vol. 43(3), pp [6] J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp Books [7] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Second Edition Prentis Hall, (2002). 5