SURVEY OF DIFFERENT TECHNIQUES FOR GLAUCOMA DETECTION AND APPROACH FOR GLAUCOMA DETECTION USING RECONFIGURABLE PROCESSOR

SURVEY OF DIFFERENT TECHNIQUES FOR GLAUCOMA DETECTION AND APPROACH FOR GLAUCOMA DETECTION USING RECONFIGURABLE PROCESSOR LATA B. HANCHINAL 1 PROF. NATARAJ A. VIJAPUR 2 Dept. of Electronics and communication Dept. of Electronics and communication KLE Dr. M. S. S. C E T, Belagavi KLE Dr. M. S. S C E T Belagavi lbhanchinal@gmail.com nvijapur@gmail.com DR. R.SHRINIVASA RAO KUNTE Professsor & Principal, J.N.N.C.E., shivamogga Abstract Glaucoma is a second leading cause of permanent blindness worldwide. It is characterized by elevated intraocular pressure. Aqueous humor secreted in the eye provides oxygen and vital nutrients to cornea and lens, and it is drained out through drainage canals of the eye, when these drainage canals become blocked aqueous humor cannot drain away normally, which leads to the elevation of intraocular pressure (IOP) this elevated pressure destroys the optic nerve, in turn causes the enlargement of optic cup and loss of vision. For the patient with glaucoma optic cup size increases and optic disc size will remain same, hence the cup to disc(cdr) value will be high for glaucoma patient compared to normal fundus image. Inferior Superior Nasal Temporal(ISNT) is the another parameter used for detection of glaucoma and which is determined by the ratio of blood vessels in Inferior Superior to Nasal Temporal side, for the patient with glaucoma most of the blood vessels will shift towards Nasal side, hence this ratio will be less for glaucoma patient. In this paper the different techniques for detection and classification of glaucoma are presented. We have also proposed identification of glaucoma using the reconfigurable processors. Index Terms Cup to Disk Ratio (CDR), Inferior Superior Nasal and Temporal (ISNT), K-means clustering. INTRODUCTION survey, glaucoma is the second leading cause of blindness. Glaucoma is a potentially blinding disease which is affecting more than 66 million persons worldwide. Glaucoma when compared to other diseases progresses with no pains or any other noticeable symptoms. Glaucoma is an eye disease which occurs due to the increased or decreased fluid pressure inside the eye as shown in the Fig 1.1 [7] The pressure inside the normal eye is below 21mm of Hg, when the pressure inside the eye increases more than this value optic nerve will be damaged as shown in Fig 1.1.This can permanently damage the vision the effected eyes and leads to the blindness if left untreated. Therefore, early Glaucomatous eyes are difficultly diagnosed. Most of the sufferers are unaware that they have Glaucoma until late in the course of the disease. Epidemiology surveys in North America and Europe showed a proportion of Glaucoma patients who had previously gone undetected between 50 and 75% and the proportion estimated in developing countries is even larger. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, early Glaucoma detection is a necessary way in preventing blindness and diminishing the costs for surgical treatment of the disease. Glaucoma is a chronic and irreversible disease characterized by degeneration of optic nerve cells which changes the optic nerve head and visual field [1]. Patients with early glaucoma do not have any visual signs or symptoms. Progression of the disease results in loss of peripheral vision and eventually total blindness. According to the World Health Organization 79

Fig-1.1 Intraocular Pressure variation According to the world health organization (WHO) survey glaucoma is the leading cause of blindness that contributes to approximately 5.2 million cases of blindness and will increase to 11.2 million people by 2020.Though much work is reported, still there is a need to develop new efficient approaches to detect Glaucoma and to develop a cost effective system which assist the ophthalmologists. Various tools used for image processing are MATLAB, DSP processor and OPENCV (open source system for computer vision).matlab is a very powerful tool for image processing, it is a high level programming language which is easy to use and displaying results. Digital signal processor is a generic processor which is used for computation Of real time data. Opencv [6] was released in 2006 with collaboration between computer vision researchers and Intel. It currently contains more than 2500 algorithms addressing different aspects of computer vision. highgui, imgproc, are the main libraries used from OpenCV. The optimized functions in OpenCV increase the speed of operation and are very much suitable for real time mass screening purpose. I. EFFECTS OF GLAUCOMA a) Increase in the CDR Diameter in vertical direction. b) Reduction in the RDR value. c) CUP to DISC area ratio. d) ISNT ratio. a) CDR is defined as the ratio of vertical cup diameter to vertical Disc diameter. CDR is one of the most important indicator of the Glaucoma, because as Glaucoma advances the CUP area enlarges until it occupies the most of the DISC area and there will be no change in the DISC size as shown in figure1.2 Fig 1.2 - Increase in CUP size in vertical direction. b) Neuroretinal RIM is the area between the CUP and DISC boundary of the eye [8]. Thinning of the neuroretinal RIM is also one of the sign of the glaucoma. With the regional preference for superior and Inferior poles of the DISC RDR is calculated for the Inferior and superior quadrant as the ratio of RIM to DISC area as shown in figure 1.3 Fig 1.3 - Neuroretinal RIM c) CDR Area ratio is measured by calculating the ratio of CUP area to the DISC area by counting the total number of pixels from the segmented CUP and DISC area as shown in figure 1.4 Fig 1.4 - CUP and DISC area d) ISNT ratio is calculated using the ratio of area of blood vessels in Inferior superior to Nasal Temporal side of the optic DISC. There may be shift in the blood vessels to nasal side in Glaucomatous eye and this ISNT ratio will be less compared to the normal eye ISNT ratio as shown in figure 1.5. With all above mentioned parameter variation they will affect the optical nerve which will permanently damage the eye sight 80

of the person which will leads to blindness, few of the effects of Glaucoma is shown in figure 1.5 below. Fig 1.5 Effects of Glaucoma disease II. LITERATURE SURVEY There are already many techniques are used to detect the Glaucoma. In 2-D images segmentation of cup and disc presents a major problem. K. Narasimhan et. al [1] proposed semi automated method for glaucoma detection using CDR and ISNT ratio of a fundus image. Here Region Of Interest is extracted using and K-Means clustering technique. Further elliptical fitting is used to segment cup and disc. Matched filter and Local entropy thresholding is applied to extract blood vessels. Here OpenCV (Open Source Computer Vision Library) a library of programming functions developed by Intel. Core, is used for implementation. Experimental results show that a sensitivity of 90% is obtained from the predefined set of images. The performance of the system can be improved by using better trained classifier. Darsana S et.al [2] proposed automatic glaucoma assessment by combined analysis of fundus eye image and patient data. In image level processing technique Fundus image feature extraction and ocular parameter evaluation are carried out and features used are Cup to Disc Ratio (CDR), Rim to Disc Ratio (RDR), and Inferior Superior Nasal Temporal (ISNT) ratio of blood vessels in disc region. In the Data level programming Data level classification is done using the risk calculator. Here each patient is being classified into one of the three levels of glaucoma risk they are LOW, MODERATE, and HIGH. After classification a weighting score is assigned for each class for further analysis. In the combined level programming is the final classification and determines the risk of glaucoma for the patient. It is obtained by the sum of scores obtained in image level and data level classification. The code is programmed in MATLAB the results obtained are 97.7% sensitivity. By considering few other parameters of the retina using 3D images sensitivity can be improved, the main barrier for such a work is the unavailability of 3D images and high cost of obtaining 3D images. Hafsah Ahma et al [3] proposed image processing technique for the detection of glaucoma which mainly affects the optic disc by increasing the cup size. Glaucoma is categorized through extraction of features from retinal fundus images. The features include Cup to Disc Ratio (CDR), and Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants for verification of the ISNT rule. In the proposed method original image green plane is extracted for extraction of optic cup and then converted to gray scale image. Optic cup having the brighter contrast with respect to others in image, Threshold value for the extraction of cup varies because there is gradual transition in cup color by which boundary of cup is not much clear. Therefore, mean of this image is calculated using software and on the basis of this mean a threshold value for linearization is defined. Similarly optic disc Value plane is extracted from HSV image. The V plane is then converted to gray scale image. After that find mean value of gray scale image and then convert it to binary image. By setting threshold to 1500 unwanted objects are removed except optic disc in the resultant image. Another feature to detect glaucoma is extraction of Neuroretinal Rim (NRR). The optic disc and optic cup are already extracted now in order to extract NRR, AND operation is applied on both resultant images of cup and disc. On extracted NRR image a mask of size 256x256 is applied to measure the ratio of area covered by neuroretinal rim in ISNT quadrants. From the measured values ISNT ratio is calculated. The code is programmed in MATLAB method achieves an average more factors which reflects the glaucoma symptoms. MATLAB method achieves an average accuracy of 97.5% having an average computational cost of 0.8141 seconds. Proposed work can be extended by integrating more factors which reflects the glaucoma symptoms. The factors that can be included are notching, disc hemorrhage, inter eye symmetry etc. S. Savita et.al [4] proposed two automatic methods for extraction of Disc for CDR calculation. The component analysis method and region of interest (ROI) based segmentation is used for the detection of disc. For the cup area detection, component analysis method is used. Later the active contour is used to plot the boundary accurately. For extraction of optic disc manual thresholding method is used, by removing the blood vessels in the retinal images. The morphological operation such as the dilation, erosion, is performed. The morphological functions are applied to do the pre-processing. Erosion is done to contrast the boundary of the object. The result of this operation has a smooth image without any blood vessels. Similarly for Disc analysis color component method is used In the component analysis method, RGB components of the images are analyzed, and it was found that the optic disc was more easily discriminated in RED image. In order to measure disc more accurately, the blood vessels in the image has to be removed. This is achieved by performing the closing and opening operation. Closing is similar to dilation. Opening is similar erosion. It was found that ROI, combined with the component analysis method provides better estimation of CDR. The method has been applied to nearly three hundred images and the CDR was correctly identified. The implementation of the above said method would be more Fruitful with the availability of more suitable data. 81

Dr Ravi Subban et.al [5] proposed image processing technique for retinal blood vessels detection using features extraction techniques, mathematical algorithms and artificial neural network classifiers. A review and study is made on human retinal blood vessel detection techniques in the research perspective. The blood vessels extracted by the original local entropy thresholding method are usually not complete and some detailed structures are missed. Therefore two modifications are introduced to improve the results of blood vessel extraction that is essential to increase the performance of algorithm. First, the co-occurrence matrix definition is modified to increase the local entropy. The cooccurrence matrix of an image shows the intensity transitions between adjacent pixels. The original co-occurrence matrix is asymmetric by considering the horizontally right and vertically lower transitions. Here, some jittering effect is added to the co-occurrence matrix that tends to keep the similar spatial structure but with much less variations. Then by considering the sparse foreground, the optimal threshold is selected. The original threshold selection criterion aims to maximize the local entropy of foreground and background in a gray-scale image without considering the small proportion of foreground. Therefore, selecting the optimal threshold that maximizes the local entropy of the binary image indicating the foreground/background ratio is determined. The larger the local entropy, the more will be the balanced ratio between foreground and background in the binary image In order to handle the image data we are converting the RGB mode image to gray scale image and filtering is used to avoid the unwanted elements of the image and also smoothens the image for further processing block. Feature extraction In this stage we are identifying the region of interest (ROI). In our case ROI is the ratio of the CUP and DISC area which is identified by applying K-mean clustering algorithm recursively or by using thresholding method. The green plane is extracted from original image for extraction of optic cup, which provides enhanced contrast for optic cup. The original image was then converted to HSV plane. It was concluded after analysis of a number of images, that optic disc has a better contrast in V plane extract from HSV image. K-Mean Clustering algorithm K-means algorithm that finds the centers of n clusters and groups the input samples around the clusters is to define k centroids [9], one for each cluster. At this point k new centroids are calculated as the mean of the clusters resulting from the previous step. As a result of repetitive application of these two steps, the k centroids change their location step by step until no more changes take place. K-means clustering plays a vital role in the feature extraction stage to compute one of the features CDR. It is an unsupervised learning algorithm that solves the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters fixed a priori implements Flow chart for K- Mean clustering algorithm is as shown in the figure 1.7. III. PROPOSED METHODOLOGY In the proposed method we are using CUP to DISC area ratio for identifying the glaucomatous eye. The block diagram of the proposed system is as shown in the figure 1.6 in the proposed methodology we have 5 steps of operations and they are image acquisition, gray scale conversion and filtering, feature extraction, processing block and result evaluation and the database management. Fig 1.6 Block diagram Image acquisition The retinal images have been taken by fundus camera in RGB mode. The size of the fundus image is 1504x1000 pixels. G plane is considered for the extraction of optic disc and optic cup, as it provides better contrast than the other two planes. Hence it is necessary to separate the G plane for further analysis. Filtering and image acquisition Fig 1.7 Flow chart Processing block The CDR which is the ratio between the area of the optic cup and the area of the optic disc is computed and used as one of the features for the detection of glaucoma. For a patient with glaucoma Optic Cup size increases while the Optic Disc size remains same and hence CDR will be high for glaucoma patient than normal fundus image. Then by K-means algorithm which is an iterative technique that is used to partition the ROI image into K clusters. The ROI covers mainly the entire optic disc, optic cup and a small portion of other regions of the image. 82

Data base In order to keep the record of the patient s data on regular visits and to indicate the progression of the disease database is required. After every computation stage we have to store the information into database which can help the ophthalmologist to assist during next visit of the patient. With the database information even we can evaluate the risk level of the glaucoma as Low, Moderate and High. In the proposed method we are using open CV for processing the image. OpenCv is preferred over matlab for its optimized functions which increases the speed. This study can be automated by using standard classifier and hardware can be developed by dumping the code into Reconfigurable system for eye disease Glaucoma detection into a processor which supports OpenCV. IV.ADVANTAGES 1. Proposed method can be used for early detection of the Glaucoma so that we can save the vision of the patient. 2. Low cost and improved computation time can be achieved using the proposed method. REFERENCES [1] K. Narasimhan, K. Vijayarekha, K.A. JogiNarayana Research Journal of Applied Sciences, Engineering and Technology 4(24): 5459-5463, 2012 Glaucoma Detection From Fundus Image Using Opencv. [2] Darsana S, Rahul M Nair International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 0882 Volume 3, Issue 2, May 2014 A Novel Approach towards Automatic Glaucoma Assessment. [3] Hafsah Ahmad, Aqsa Shakeel, Syed Omer IEEE International Conference on Robotics and Emerging Allied Technologies in Engineering (icreate) Islamabad, Pakistan, April 22-24, 2014 Detection of Glaucoma Using Retinal Fundus Images [4] S.Kavitha,S.Karthikeyan, Dr.K.Duraiswamy 2010 Second International conference on Computing, Communication and Networking Technologies. Early detection of Glaucoma in Retinal images using CUP to DISC ratio [5] Dr Ravi Subban, G. Padma Priya, P.Pasupathi, S.Muthukumar International Journal of Engineering, Business and Enterprise Applications (IJEBEA) Research Perspective Review on Retinal Blood Vessel Detection. [6] Open Source Computer Vision Library: http://opencv.org. [7] Archana Nandibewoor S B Kulkarni Sridevi Byahatti Ravindra Hegadi Proceedings of the World Congress on Engineering 2013 Vol III, Computer based diagnosis of Glaucoma using digital fundus image. [8] A.Murthi & 2M.Madheswaran International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 2012, Coimbatore, INDIA Enhancement of optic cup to optic disc ratio detection in Glaucoma diagnosis [9] Ms.Chinki Chandhok, Mrs.Soni Chaturvedi, Dr.A.A Khurshid International Journal of Information Technology (IJIT), An Approach to image segmentation using K-Mean Clustering algorithm. 83