RETINAL LESION DETECTION IN DIABETIC RETINOPATHY ANALYSIS I. INTRODUCTION

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1 Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), ISSN International Conference on Knowledge Discovery in Science and Technology 2019, ICKDST '19, Pune Manisha Laxman Jadhav Assistant Professor,Department of E&TC Engineering MET s Institute of Engineering Nashik(M.S.), India manisha.shinde1@gmail.com M. Z. Shaikh Principal, bharti Vidyapeeth College of Engineering CBD, Belapur, Navi Mumbai, Imdia mzshaikh2003@yahoo.co.in ABSTRACT- Medical image analysis is a very well-known research area in these days in which digital images are analyzed for the analysis and screening of different medical problems. Diabetic Retinopathy (DR) is an eye disease caused by the increase of glucose in blood and may cause loss of sight. A computerized system for the early detection of DR can save patient s sight and can also help the ophthalmologist in screening of DR which contains different types of wound, i.e., microaneurysms, hemorrhages, exudates. This paper presents a method for detection and classification of exudates in coloured retinal images. It eliminates the duplication exudates region by removing the optic disc region. The detection of optic disc is an essential for this approach which has been detected by morphological operations. Exudates are found as bright spots and the classification of exudates is done with exudates feature. The proposed system is evaluated and tested on the real time database taken from eye hospital in Nashik and Pune (Maharashtra, India). Many techniques have been used to detect exudates. M Sridevi [2] proposed a method by applying Histogram equalization twice followed by segmentation to make exudates visible. An approach to detect exudates by the combined region growing and edge detection is proposed [3]. Segmentation of exudates is by thresholding and classification is done by using SVM classifier [6].The examining of diabetic patients for the development of diabetic retinopathy can potentially reduce the risk of vision loss in these patients. The control on DR can be achieved by earlier diagnosing and treating by seeking medical treatments. Keywords- Diabetic Retinopathy (DR), Exudates, Morphology. I. INTRODUCTION One of the most leading cause of blindness in the world is Diabetic Retinopathy(DR). During the first twenty years of having diabetes, nearly all patients with type I and more than sixty percent of patients with type II will develop Diabetic Retinopathy. Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR), 3.6 % of type I and 1.6% of the type II turned legally blind. In type I, 86% of blindness was attributed to DR while in type II, one third of the cases were due to DR [1]. The Fundus camera is the basic requirement for DR detection, as it is friendlier to Patient. The microaneurisms and hemorrhages are red lesions and bright lesions are Exudates. The microaneurisms(mas) are observed as swelling to retinal capillaries and appear as red dots in the retinal Fundus images. The hemorrhages are formed due to leaking blood through retinal capillaries which are slightly larger than MAs. The retinal capillary walls leaks the fluid reach in lipids and proteins leads to Exudates. Visually exudates are perceived as yellowish or white colour with different sizes and shapes. and locations. All these abnormalities are highlighted in the in figure.1. Fig.1. Fundus image which depicts the anatomical structures in the human retina This paper proposes a mathematical morphology based exudates detection on real time retinal images of nondilated Pupil, as morphology based operations are less complex in computation, which can work efficiently on very low speed systems. This enables the Ophthalmologists in rural areas to diagnose the disease effectively. The position of exudates explains the severity of disease, such as, if the the exudates are near the macula region the chances of getting blindness are more. 1. II. PROPOSED METHOD Manisha Laxman Jadhav and M. Z. Shaikh 1

2 2. All the retinal images are taken from ophthalmologists at NAVKAR hospital Nashik (M.S.) & Sahyadri Hospitals Pune (M.S.). Also the algorithm is tested with standard database such as HRF, DRIMDB datasets. A. Pre-processing The images from each dataset are RGB images and that need to be converted to intensity image, where the green, red and blue planes are separated. Out of three planes, green plane carries good contrast image and more details of the image. So for further processing the green plane is considered. As the images are low contrast images, it needs filtering followed by histogram equalization. As the linear filter blurs the edges, in order to preserve the edges, median filters are used to remove noise by preserving edges.[7] Contrast limited adaptive histogram equalization (CLAHE) is used, as it works on small areas rather than entire image. [Gonzalez] Exudates are normally high intensity features, appears with same intensity as that of optic disc. So after enhancing contrast, it is assigned with high intensity values. So before detecting the exudates it is necessary to detect and eliminate optic disc. B. Optic disc elimination Optic disc possesses highest intensity circular shape in the fundus image. It is needed to be removed in order to detect the exudates. Morphological closing operation is performed on the image with disc shaped structuring element, which removes the vessels present in the optic disc region, as shown in figure.2. (c) (d) (1) X B =(X B) B Where B is morphological structuring element operated on input image X. The resulting image is binarized by means of thresholding, the thresholded image is then applied on the input image which act as mask. The mask is applied on the input image after inversion and then the candidate bright region is masked after dilating the image, acts as marker image. The marker image is overlaid on mask to reconstruct the image, further the the image is dilated with disc shaped structuring element and then eroded with structuring element of line shape. The reconstructed image is formed as shown in fig.the reconstructed image is further subtracted from original filtered image as shown in fig. In the result the high intensity areas are reconstructed and lower intensities are removed. (e) (f) Manisha Laxman Jadhav and M. Z. Shaikh 2

3 Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), ISSN International Conference on Knowledge Discovery in Science and Technology 2019, ICKDST '19, Pune Exudates can be identified as lesion caused by diabetic retinopathy. They are visible as shiny, yellow intra retinal protein deposits. They normally appear near the leaking capillaries within the retina. The main cause of exudates is proteins and lipids leaking from the blood into the retina via damaged blood vessels Exudates are major cause of vision loss, with variable size, shape, and position.[8,9]. The vessels with high contrast can be eliminated by using closing operation on the low pass filtered image before optic disc removal. The local standard deviation is applied on the resultant image to obtain the largest circular shape along with cluster of exudates as shown in fig.3. (f) (4) Fig. 2. a) Original image, b) Green channel image, c) Contrast enhanced image, d) Image after removal of noise, e) Vessel removal, f) biggest circle in the image, g) Optic disc detected image As the optic disc can be easily identified being largest area. But in some cases, where the exudates are in larger in size, it may be falsely detected. So the detection of optic disc must be more specific. In order to do that circularity of shape of the region have been defined for the value, S, it can be defined by using following equation: S= ; (2) Where A= Total no. of pixels in the region and P = Total no. of pixels around periphery of each region. Where w(x) = sub-window of size,µf1(x) is mean value of f1(i). Further the image is thresholded using otsu algorithm [10]. So that, all the regions with low local variations are eliminated. In order to certify that all neighbouring pixels of the thresholded result were also included in the candidate region, binarization operation is applied on the image after that the image is dilated using disc type structuring element as shown in fig.3. The target region in the image is not only the borders but the entire region; therefore the inner area is filled. The previously detected optic disc area is dilated and subtracted from above result in order to obtain the the filled areas separating optic disc from the image.fig 3(c). The dilation operation is applied on the largest circular shape in order to cover all pixels in the optic disc. For this a flat disc shape structuring element is used. (3) Where X1 is the resultant image of high intensities with rest area removed The optic disc area in the image was masked out using the above output the result is shown in fig 2(g) C. Exudate Detection Manisha Laxman Jadhav and M. Z. Shaikh 3

4 III. RESULT Total 45 images from Sahyadri hospital Pune, 70 images from Navkar hospital Nashik and 120 imges from DRIMDB dataset are tested. Same algorithm is tested on normal images. The output with normal images is as shown in fig.5 (c) Fig.3 a) Thresholded image after local variation, b) The image with filled areas, c) Detected Exudates with removal of optic disc (5) Where f4(x) - Resultant image obtained by subtracting optic disc f3(x) - The dilated image with region filled - Optic disc detected image Further the f4(x) is morphologically reconstructed using dilation operation upon the original image as shown in fig.4. (c) (d) (e) (f) Fig.5 a) grey scale image b) vessel removal after closing operation c) optic disc removal d) Detected Exudates, e)exudates after dilation operation f) Exudates superimposed on original image. Fig.4 a) The resultant image after subtracting from original image a) The result superimposed on original image. Images from Navkar hospital Area of Exudates Manisha Laxman Jadhav and M. Z. Shaikh 4

5 Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), ISSN International Conference on Knowledge Discovery in Science and Technology 2019, ICKDST '19, Pune Nashik Image 1 83 Image Image Image Image Image 6 54 Image Image Image 9 35 Image Images from Sahyadri Hospital Pune Area of Exudates Image Image Image Image Image Image Image Image Image Image Table No.1 Exudates Area calculated for DR Images of different images. The detected exudates in normal image are negligible as shown in fig 5(f). The area of detected exudates from DR images is calculated. Based on area calculation the decision is made whether the image is DR image or not. IV ACKNOWLEDGEMENT We would like to thank Navkar Eye Hospital,Nashik and Sahyadri Hospital Pune, who supplied all the images used in this project for its great support for the project. V REFERENCES [1] D. S. Fong et al., "Retinopathy in diabetes," Diabetes Care, vol.27, pp. s84-s87, [2] M.Sridevi Maheshwari, Adarsh Punnolil, A novel approach for Retinal Lesion detection Diabetic Retinopathy Images IJIRSET Vol.3,Special issue 3, March [3] H.Li and O. Chutatape, Automated Feature Extraction in Color Retinal Images by a Model Based Approach IEEE Trans. on Medical Engineering, 2004, 51(2), [4] P.N.Jebarani Sargunar, Automated grading of diabetic retinopathystages in Fundus images using SVM classifier. International Journal of Recent Trends in Engineering, 2009, 2(4), [5]Hussain F. Jaafar, Asoke K. Nandi and Waleed Al-Nuaimy Automated detection and grading of Hard exudates from retinal fundus images 19 th European signal processing conference,barelona,spain Aug 29-2 Sept [6 ] Vesna Zeljković1, Milena Bojic2, Shengwei Zhao1, Claude Tameze3, Ventzeslav Valev4 Exudates and Optic Disc Detection in Retinal Images of Diabetic Patients Article in Concurrency and Computation Practice and Experience: January 2015 DOI: /cpe.3215 [7] Shahriar Badsha & Ahmed Wasif Reza & Kim Geok Tan & Kaharudin Dimyati A New Blood Vessel Extraction Technique Using Edge Enhancement and Object Classification Digit Imaging (2013) 26: DOI /s [8] Osareh, A.; Mirmehdi, M.; Thomas, B.; Markham, R. Automated Identification of Diabetic Retinal Exudates in Digital Colour Images. Br. J. Ophthalmol 2003, 87, [9] Gardner, G.G.; Keating, D; Williamson, T.H.; Elliot, A.T. Automatic Detection of Diabetic Retinopathy using an Artificial Neural Network : a Screening Tool. Br. J. Ophthalmol 1996, 80, [10]Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. on Syst. Man. and Cybern 1979, SMC- 9, [11] R.C. Gonzales, R.E. Woods, Digital image processing (Upper Saddle River, NJ: Addison-Wesley publishing Co., 2002). [12] Akara Sopharak ; Bunyarit Uyyanonvara, Sarah Barmanb, Thomas H. Williamson Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using Manisha Laxman Jadhav and M. Z. Shaikh 5

6 mathematical morphology methods. Computerized Medical Imaging and Graphics 32 (2008) [13] Dr. Yogesh Rajput,Dr. Ramesh Manza,Dr. Pravin Yannawar, Dr. Dynaneshwari Patil. Undrstanding Programming aspects of Diabetic Retinopathy using MATLAB SPD Publication. [14] Rajesh Kumar Tiwari and G. Sahoo, Data Hiding in Microsoft Word File, International Journal of Information and Computer Security, Volume 4 Issue 3, [15] Kumar Gunjan, G. Sahoo and R. K. Tiwari, Identity Management in Cloud Computing A Review, International Journal of Engineering Research & Technology, Vol. 1 No- 4, [16] Rajesh Kumar Tiwari and G. Sahoo, A Novel Steganographic Methodology for High Capacity Data Hiding in Executable Files, International Journal of Internet Technology and Secured Transactions, Vol. 3, No. 2, 2011, pp Manisha Laxman Jadhav and M. Z. Shaikh 6