Detection of Optic Disk in Fundus Image Using Supervised Learning S

Journal of Seybold Report ISSN NO: 1533-9211

DETECTION OF OPTIC DISK IN FUNDUS IMAGE USING SUPERVISED LEARNING S. LEEMA JEYA ROSY1 PG Scholar, Department of ECE, PSNA CET, Dindigul, Tamil Nadu, India1

DR. P. N. SUNDARARAJAN2 Professor, Department of ECE, PSNA CET, Dindigul, Tamil Nadu, India2

Abstract: Glaucoma is a sight intimidating retinal requires fundus image. infirmity that needs attention at its early stage, regardless Often, low quality images lead to terrible showing of not admitting any symptoms other than slow vision. programmed analysis. Thusly, it is important to reestablish the Glaucoma is recognized for the most part through the cup image for better investigation. The quality of retinal images to disc ratio dependent on image processing techniques. is often affected by many of the factors. The retinal images This work includes segmentation of optic disk region from were obtained by the illumination procedure. Here the retina the fundus image, this is done by thresholding, the features absorbs the illuminated light after passing it through the lens were extracted from these segmented structures which uses and reflects that light back to the fundus camera. This convolutional neural networks, and then feature selection reflection of light is captured in order to shape the image. In is done. Here the feature extraction involves edge detection any case, the human eye is anything but an ideal optical and followed by the classification which is performed by framework and the illumination got by the fundus camera is employing the decision trees in order to detect whether the regularly constricted along the way of the light. This does not input fundus image is affected by Glaucoma or not. The work well when the retina of human eye is influenced by measurements were obtained as, 96.62% of sensitivity, disease. The OD assumes a significant job in creating 99.96% of specificity, 96.66% of correct classification. automatic identification of Diabetic Retinopathy since segmentation is a key preprocessing segment in numerous Along with this work the feature extraction is done using algorithms intended to recognize different fundus highlights. pattern extraction method. It uses local binary features in In addition to that, the Optic Disc division may be valuable in order to extract the features from the image. It is then determining consequently a few disorders brought about by classified again using the decision trees. The measurements glaucoma. Identifying the Optic Disc is quite possibly utilized were obtained as, 97.75% of sensitivity, 99.97% of to diminish true positive points in the identification of areas of specificity, 97.77% of correct classification. retinal exuades. These wounds are an analytic key for evaluating the danger of macular swelling. Keywords: Glaucoma, Optic Disc, Thresholding, Feature extraction, Decision trees. If an individual is influenced with glaucoma, the optic cup size increments in steps and when this size of optic cup matches the size of optic disk the patient losses his 1. INTRODUCTION vision. There are numerous backhanded employments of the These days probably the most widely recognized OD division. The details about the optic disk region in the reasons for visual hindrance and visual impairment are fundus image helps to extract the features from that image. diabetic retinopathy, glaucoma, traumatic injuries, cataract, the detection of macular region from the retinal image is done hypertension and macular degeneration. These eye diseases by segmenting the optic nerve head. The focal area of the show themselves in retina and these infections can be macula is called Fovea, so that by taking the centroid of recognized through an immediate and customary recognized macula, the fovea can be detected. ONH area can ophthalmologic assessment. However numerous elements, likewise help in the identification of the major temporal for example, populace development, maturing, are adding to arcade (MTA) as the MTA location is related with macula. the expansion of the patients with these infections, which Likewise, since the Optic Disk and exudates looks similar, makes the number of ophthalmologists required for before the segmentation of exudate the Optic Disk must be evaluation by direct assessment turns into a constraining masked out. By this procedure the misclassification might be variable. Accordingly, PC supported determination frame removed. Lot of challenges occurs when the features were work which can essentially diminish the burden on the extracted automatically and segmented. Major factors that ophthalmologists. Glaucoma is a disease that harms our eye’s affect the retinal images were contrast and illumination. optic nerve. It deteriorates over time. It is one of the most These variations are due to the effects developed by the well-known explanations behind visual deficiency around cameras. the world. In a healthy eye, a clear liquid is continually being This works consists of three steps, initially made behind the iris and it leaves the eye through a the input fundus image is encountered for the pre- processing, microscopic drainage canal in the front of the eye. When this after that the morphological operations are used to provide channel is blocked, the pressure inside the eye gets increased the binary image. Then the DT (decision tree) is used to and often cause damage to the optic nerve. This optic nerve classify the resulting binary images. More over in this associates the eye to the cerebrum in the brain. Any harm to proposed method the evaluations are done using two public this nerve leads to vision loss. Early forecast of Glaucoma is datasets STARE and DRIVE. required as the renewal of harmed optic nerve fiber is not possible. The disease once in a while causes manifestations until later phases of the sickness. The Glaucoma diagnosis 1

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2. EXISTING METHOD image are associated and piecewise linear, Gabor filters are more qualified because the veins in retinal images are In the available unsupervised techniques, methods associated and linear. They are also fit for distinguishing that put in vessel tracking morphological operations, oriented features and can be adjusted to explicit frequencies. matched filtering, model-based procedures are It is conceivable to remove the background noise due to their overwhelming. In the matched filtering-based technique, the frequency delicate-ness. blood vessels were enhanced by using the gaussian filter and their derivatives. For this technique a 2-D linear structuring Various systems of segmentation of retinal images component is utilized. The vessel boundaries were extracted have been explored up until now. They are filter based from the retinal image by rotating eight to twelve times the techniques, techniques used for tracking the blood vessels, structuring element. These strategies demand that by using classifier-based techniques, morphological techniques. The the thresholding methods or by directly using the cursor the strategies use the earlier information, for example, contrast start and end search points are chosen. among the veins and background, convention of blood vessels from the similar point which is known as optic disc and the 2.1 VESSEL TRACKING: network of blood vessels. Vessel tracking techniques give precise estimations 2.4 FILTER BASED METHODS: of thickness of the vessels however tracking techniques always ends up in branch points. Classifier-based techniques Filter based methods utilize a two-dimensional is done in two stages. At first the segmentation step is done image that has a Gaussian cross-profile section in order to regularly by utilizing one among the referenced matched distinguish the veins. The gaussian kernel is turned into filter- based techniques and also the numerous features are various directions in order to fit into veins of various layout used to characterize the regions. to get an enhanced image. Now the blood vessels are extricated from the background by using the thresholding In the subsequent stage, by using chosen features techniques. This functions admirably images of healthy along with the training information, the successive forward retina, however in sick states, for example, number of false method is utilized to build the neural network classifier to positives are discovered in case of diabetic retinopathy. recognize the vessel pixels. These strategies experience the ill effects of issues 2.2 MATHEMATICAL MORPHOLOGY: related with recognizing compact and convoluted vessels that are inclined to modifications in background illumination. Segmentation of veins is done by using Individual segments are recognized using a pursuit technique mathematical morphology technique. These techniques which screens focal point of the vessel and makes certain exploit features of the vasculature shape that are referred to judgement about the path of the vessel depending on the earlier. They function admirably on ordinary fundus images particular vessel properties. with uniform contrast but, this function does not work well

when there is a noise. Many of the papers have detailed work 3. PROPOSED METHOD about the segmentation of blood vessels, and there occurs a possibility of extending those procedures into new This procedure is primarily used to analyze the vein methodologies in order to recognize the blood vessels along (blood vessel) and optic disk of the retinal fundus image. This with the artifacts. Additionally, due to the sores and other is basically used to analysis the hypertension, pathological changes the detection process turns out to be arteriosclerosis, cardio vascular disease and so forth. This substantially more complicated that influences the retinal procedure comprises of significant three things, at first the images. image is pre-processed which is followed by segmentation by using the morphological methods. After this the features 2.3 VESSEL ENHANCEMENT: were extracted from the segmented images by using edge The retinal vessel detection strategy includes two features and pattern extraction methods. stages, first is vessel enhancement, and second is The convolutional neural network uses these thresholding. The Gabor filters are set to specific frequency, extracted features. Finally, the images were classified by after this the orientations are utilized to upgrade the veins using the decision trees. As a classification result, the images stifling the background. The blood vessels were segmented were reported that if the fundus images are infected by by using the entropy-based thresholding along with gray glaucoma or not. Here, the feature extraction method is level co-occurrence matrix. In the colour retinal images, veins performed with both edge features and pattern extraction seem pitch black than the background like the shade of sores. method and hence the results were compared. In this way, it gets fundamental to absolve the veins during the identification of sores to evade false positive. Just one 3.1 DATA FLOW DIAGRAM: stage is engaged with the pre-processing of retinal images for

segmentation of vessels. This may be very well found that the veins show up most differentiated in the green channel than in red and blue channels. Just the green channel picture is utilized for additional refinement. The Gabor filters are broadly employed to image processing for example, texture segmentation, marks in character acknowledgment and streets in satellite picture examination. For the reason that, the blood vessels in fundus 2

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Input 3.5 SEGMENTATION:

Image Data Set Image Blood vessel segmentation of fundus images assumes a Resize significant job in the determination of eye infections. In this paper, a supervised segmentation of blood vessel method for

Preprocessing fundus images is presented. At first, a feature vector which is Morphological multidimensional is developed with the green channel Filtering illumination and then the morphological operations is used to construct the vessel enhanced intensity feature. OD Segmentation Structured 3.6 FEATURE EXTRACTION: Learning Size of In machine learning, pattern recognition and in OD image processing, feature extraction uses a primary set of estimated information and constructs extracted values Feature Pattern Extraction Extraction (features) proposed to be factual and concise. By using the feature extraction methods often, it proves to be better than Edge Feature extracting the features manually. Here the features like blood vessels were extracted. Edge detection is an image Decision Classification Trees processing method used for detecting the boundaries of objects within the images. It works by finding the discontinuities in brightness. At first the edges are detected TP, TN, and by using the filters the images are enhanced. By this the Accuracy, Estimation sharpness of the image is increased and it becomes clearer. Sensitivity, Specificity Local binary pattern is a sort of simple and effective visual descriptor which labels the pixel by thresholding and assumes the results to be binary, this is utilized for Fig.1. Flow diagram classification. It is the certain case of texture spectrum model. 3.2 INPUT IMAGE: It has been seen as an amazing component for texture classification. Local Binary Pattern is associated with the The primary phase of any image processing procedure is the histogram of adjusted gradients descriptor that upgrades the image procurement stage. The digitization of visual performance of detection on some data sets. characteristics of an input image is called as image acquisition. After the picture has been acquired, different CNNs are standardized variants of multilayer perceptrons. strategies for preparing can be used to perform various vision Multilayer perceptrons regularly mean completely related assignments. First Capture the Input Image from source structures, that is, every neuron in one single layer is related record by utilizing uigetfile and imread function. with neurons in the following layer. Even though, CNNs Nevertheless, if the image has not been obtained acceptably, adopt a substitute procedure towards regularization, they take at that point the planned tasks may not be attainable, even an advantage of the hierarchical pattern in data and arrange with the guide of some type of image enhancement. dynamically complex examples utilizing smaller and simpler patterns. 3.3 IMAGE RESIZE: After the input image is fed to the system, pre-processing step 3.7 CLASSIFICATION: continues. The input image is pre-processed in order to make In machine learning, classification is the issue of perceiving it prepared for further operations. This pre-processing to which set of classes (sub-populations) a new data belongs, includes image resize and filtering. Here the original image according to the training sets containing perceptions (or is resized smaller, so that the image may attain equal and instances) whose classification is known. Decision tree uniform dimensions. After the image is resized, the gaussian builds classification or regression models as a tree structure. filter is applied since, it removes the noise in the image faster It splits up an informational collection into smaller and than any other filters. This pre-processing is completed smaller batches while simultaneously a corresponding without loss in the image quality. decision tree is steadily evolved. Each node has two or more branches. Leaf node indicates a classification or decision. 3.4 VESSEL ENHANCEMENT: Decision trees are a sort of Supervised Machine Learning Image enhancement is also a part of pre-processing stage. technique where the data is constantly parting as specified by Enhancement have the meaning as improvement in the certain variable. Furthermore, the decision tree vertices are quality of the image. This image enhancement technique is the place the information split. used here because it improves the contrast of the border shapes. This procedure is highly helpful in the blood vessel 3.8 PERFORMANCE ESTIMATION: enhancement. It can be done in two methods, one by using Accuracy: The accuracy is given as the capacity to separate spatial domain and other by frequency domain. Spatial the patient and sound cases effectively. To appraise the domain works on pixels whereas, frequency domain works exactness of a test, we ought to compute the extent of true by Fourier transforms. positive and true negative in completely assessed patients. It can be expressed by the following equation. Accuracy = (TP+TN) / (TP+TN+FP+FN) --- Eq.(1) 3

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Sensitivity: The sensitivity of a test is its capacity to decide 4.3 OPTIMIZED AND ENHANCED IMAGE: the patient cases effectively. To evaluate it, we ought to Optimizing images is a process of decreasing the compute the extent of true positive in-understanding size of the image files without the sacrifice in its quality. This patients. It can be expressed by the following equation. results in providing high-quality images in the appropriate Sensitivity = (TP) / (TP + FN) ---Eq.(2) format, dimension, size, and resolution. Adaptive thresholding is used so that the image contrast is enhanced. Specificity: The specificity of a test is its capacity to decide For this “adapthisteq” is used, this enhances the contrast by the sound cases effectively. To appraise it, we ought to modifying the values in the intensity image I. ascertain the extent of true negative in solid patients. It can be expressed by the following equation. Specificity = (TN) / (TN + FP) ---Eq.(3)

4. SIMULATION RESULTS AND DISCUSSION

4.1 INPUT: a) Optimized Image The main file is runned in MATLAB 2015b software and the input image is chosen from the folder in which the dataset images were saved.

Fig.2 Input Image 4.2 FILTERING: b) Enhanced Image

Pre-processing step includes filtering and resize of Fig.5 Image Enhancement the input fundus image. The noise is removed by gaussian filter. Since it reduces the noise faster than any other filters. Feature extraction a sort of dimensionality decrease

that effectively speaks to fascinating parts of an image as a conservative feature vector. CNNs are especially valuable for discovering patterns in images to perceive objects, faces, and scenes. They gain legitimately from image data, utilizing patterns to classify images and wiping out the requirement for manual feature extraction. 4.4 DETECTION OF VESSEL AND OPTIC DISK REGION: Fig.3 Filtered Image This section extracts the veins present in the fundus image. Glaucoma patients have a raised intraocular pressure. Blood vessel segmentation of retinal images In glaucoma affected patients the blood vessels are smaller assumes a significant job in the finding of eye diseases. A than ordinary. In this manner computation of the region of the multidimensional vector feature is developed along with segmented veins empowers the discovery of glaucoma. green channel illumination by the morphological operation, Blood vessel segmentation plays a significant job in the prior so that the segmentation results can be improved. identification of disease.

(a) Iterations (b) Binary OD Fig.4 Segmentation of optic disk Fig.6 vessel region 4

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4.7 ESTIMATIONS: EDGE DETECTION

Fig.7 Optic Disk Fig.10 Estimations of Edge Detection

4.5 EXUADES: An exuade is a liquid that ooze out from the circulatory system into sores or in the areas of injuries. It can be a pus-like or clear fluid. This fluid spills out of the veins and into the adjacent tissues, during an injury and leaving skin uncovered. This fluid is made from serum, fibrin, and WBC. Exuade can possibly overflow from the cuts or from zones of contamination or inflammation. The white patches in the below image shows the exuades. These are individual dots, batch of white patches, or it may also appear as small rings. They usually appear near Fig. 11 Performance Graph of Edge Detection the blood vessels in the fundus image. These exuades gets deposited on the veins of the fundus image. LOCAL BINARY PATTERN

Fig.8 Exudates Fig.12 Estimations of LBP

4.6 HEMORRHAGE: Subconjunctival bleeding, also known as subconjunctival hemorrhage, is bleeding beneath the conjunctiva. The conjunctiva contains some little, delicate veins that are effortlessly burst or broken. At the point when this occurs, blood spills into the space between the conjunctiva and sclera.

Fig.13 Performance Graph of LBP 4.8 COMPARISON TABLE: The proposed methods were compared with previously existing algorithms and the results were shown as follows, Fig.9 Hemorrhage

VOLUME 15 ISSUE 9 2020 Page: 2036 Journal of Seybold Report ISSN NO: 1533-9211 Parameters PNN KNN Edge LBP detection Imag., vol. 27, no. 2, pp. 237–246, Feb. 2008. [4] Budai. A, R. Bock, A. Maier, J. Hornegger, and G. Accuracy 89.6% 90% 96.66% 97.77% Michelson, “Robust vessel segmentation in fundus images”, Int. J. Biomed. Imag., article 154860, Sensitivity 90% 100% 96.62% 97.75% pp.1-11, 2013. Specificity 88% 53% 99.96% 99.97% [5] Helena M. Pakter, Sandra C. Fuchs, Marcelo K. Maestri, Leila

B. Moreira, Luciana M. Dei Ricardi, Vitor F. Pamplona, Manuel M. oliveira, and Flavio D. Table-I Comparison among different Algorithms Fuchs, “Computer-Assisted Methods to Evaluate Retinal Vascular Caliber: What Are They PNN- Probabilistic Neural Network Measuring?”, @ Investigate Ophthalmology KNN- K-Nearest Neighbour Visual Science, Vol. 52, No. 2, pp.810-815, February 2011. LBP- Local Binary Pattern [6] Huiqi Li*, Member, IEEE, and Opas Chutatape, 5. CONCLUSION AND FUTURE WORK Senior Member, IEEE, “Automated Feature Extraction in Color Retinal Images by a Model In this paper, we proposed a detection of glaucoma Based Approach”, IEEE Transactions on disease by performing blood vessel and optic disk segmentation Biomedical Engineering, vol. 51, no. 2, pp.246-254, from the fundus image relying on features and supervised February 2004. learning. First, the image features are extracted by using final [7] Jingdan Zhang, Yingjie Cui, Wuhan Jiang, and Le layer output and also the intermediate output thus it contains Wang. “Blood Vessel Segmentation of Retinal multiple information regarding the structure of the fundus Images Based on Neural Network”, Springer image. Second, morphological operations and structured International Publishing Switzerland 2015, Y.-J. learning is introduced for retinal blood vessel segmentation. Zhang (Ed.): ICIG 2015, Part II, LNCS 9218, pp. Third, this procedure is unmanned and trainable. This is done 11–17, 2015. by a blend of feature learning and supervised learning. [8] K.A. Vermeer, F.M. Vos, H.G. Lemij and A.M. Fourth, this technique was verified using two publicly Vossepoel, “A model-based method for retinal blood accessible datasets. At long last, this technique was exposed vessel detection”, Comput. Biol. Med., vol. 34, no. to be preferable in managing the challenges in glaucoma 3, pp. 209–219, 2004. detection, since it can extricate invariant features with strong [9] Marc Lalonde, Mario Beaulieu, and Langis Gagnon, generalization capability. “Fast and Robust Optic Disc Detection Using The advantage of this proposed work is less relied Pyramidal Decomposition and Hausdorff-Based on training data. It requires minimum time for segmentation. Template Matching”, IEEE Transactions on It achieves compatible accuracy on vessel segmentation. It Medical Imaging, vol. 20, no. 11, pp.1193-1200, presents limited false vessel pixels in the last segmented November 2001. retinal veins. The presented approach is best on top of this [10] Mendonca, and A. Campilho, “Segmentation of technique in fundus images with fundamentally enormous retinal blood vessels by combining the detection of amount of red lesions in the region of the optic nerve head. centerlines and morphological reconstruction”, In future work we will evaluate the accuracy of detecting IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200– glaucoma by using unsupervised algorithm that is it is a self- 1213, Sep. 2006. learning technique that does not require any prior set of [11] Muhammad Moazam Fraz∗, Paolo Remagnino, categories. It will be able to discover the features of input Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R. population by its own. Also to detect the stages in Glaucoma Rudnicka, Christopher G. Owen, and Sarah A. disease. Barman, “An Ensemble Classiﬁcation- Based Approach Applied to Retinal Blood Vessel Segmentation”, IEEE transactions on biomedical REFERENCE engineering, vol. 59, no. 9, September 2012. [12] P. C. Siddalingaswamy, K. Gopalakrishna Prabhu, [1] Arturo Aquino, Manuel Emilio Gegúndez-Arias, “Automatic detection of multiple oriented blood and Diego Marín, “Detecting the Optic Disc vessels in retinal images”, Boundary in Digital Fundus Images Using J. Biomedical Science and Engineering, 2010, 3, 101- Morphological, Edge Detection, and SSSFeature 107. Extraction Techniques”, IEEE Transactions on [13] R. Priya and P. Aruna, “Diagnosis of Diabetic Medical Imaging, vol. 29, no. 11, pp.1860-1869, Retinopathy Using Machine Learning Techniques”, November 2010. ICTACT Journal on Soft Computing, July 2013, [2] B. Al-Diri, A. Hunter, and D. Steel, “An active Volume: 03, Issue: 04. contour model for segmenting and measuring [14] S. Muthu Lakshmi, “Supervised Blood Vessel retinal vessels”, IEEE Trans. Med. Imag., vol. 28, Segmentation in Retinal Images Using Feature no. 9, pp. 1488–1497, Sep. 2009. Based Classification”, International Journal of [3] B. Lam, and H. Yan, “A novel vessel segmentation Advanced and Innovative Research ISSN: 2278- algorithm for pathological retina images based on 7844, Volume 1, Issue 1, June 2012. the divergence of vector fields”, IEEE Trans. Med. [15] Sandra Morales*, Valery Naranjo, Jesús Angulo, 6

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and Mariano Alcañiz, “Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology”, @ Institute of Electrical and Electronics Engineers Transactions on Medical Imaging, vol 32, no.4, April 2013. [16] S. Leema Jeya Rosy, P.N. Sundararajan, “Detection of Optic Disk in Fundus Image using Supervised Learning: Survey”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 8 Issue 09, September-2019.

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