Reviewed Paper Volume 2 Issue 12 August 2015 International Journal of Informative & Futuristic Research ISSN (Online): 2347-1697 Automated Detection Of Glaucoma & D.R Paper ID IJIFR/ V2/ E12/ 016 Page No. 4427-4436 Computer Science Subject Area & Engineering Key Words Optic Disc, Principal Component Analysis, Watershed Transformation, Vessel Segmentation, Cup To Disc Ratio (CDR) Sharon Susan Jacob 1 Rani. M. R. 2 M.Tech. Student Department Of Computer Science & Engineering Musaliar College Of Engineering & Technology Pathanamthitta, Kerala Assistant Professor Department Of Computer Science & Engineering Musaliar College Of Engineering & Technology Pathanamthitta, Kerala Abstract This paper proposes a new method for the detection of glaucoma and diabetic retinopathy using fundus image. Glaucoma is the second leading cause of permanent blindness worldwide. Early detection of glaucoma can limit the disease progression. The ratio of the size of the optic cup to the optic disc, also known as the cup-to-disc ratio (CDR), is one of the important clinical indicators of glaucoma, and is currently determined manually by trained ophthalmologists, limiting its potential in mass screening for early detection. Diabetic retinopathy is one of the serious eye diseases that can cause blindness and vision loss. Filter based approach with morphological filters is used to segment the vessels. The morphological filter is tuned to match that part of vessel to be extracted in a green channel image. To classify the pixels into vessels and non-vessels local thresholding based on gray level co- occurrence matrix is applied. The performance of the method is evaluated on two publicly available retinal databases with hand labelled ground truths. 1. Introduction Diabetic retinopathy, hypertension, glaucoma, and macular degeneration are nowadays some of the most common causes of visual impairment and blindness. Early diagnosis and www.ijifr.com Copyright IJIFR 2015 4427
appropriate referral for treatment of these diseases can prevent visual loss. Usually, more than 80% of global visual impairment is avoidable, and in the case of diabetes by up to 98%. All of these diseases can be detected through a direct and regular ophthalmologic examination of the risk population. However, population growth, aging, physical inactivity and rising levels of obesity are contributing factors to the increase of these diseases. For the evaluation of these diseases, a number of ophthalmologists use direct examination, which is a limiting factor. Optic disc (OD) detection is a key process in many algorithms designed for the automatic extraction of anatomical ocular structures and the detection of retinal lesions. Its automatic location would provide useful information to determine regions of interest in a fundus image, as well as for the early detection of certain pathologies. For example, it is directly related to diseases such as glaucoma and knowing its location would also help to reduce the number of false positives in the detection of exudates associated with diabetic retinopathy. The techniques presented in the literature about the OD processing from fundus images can be grouped into two categories: location and segmentation methods. Location methods are based on finding the OD center and segmentation algorithms on estimating its contour. Location methods are usually focused on the fact that all retinal vessels originate from the OD and follow a parabolic path or that the OD is the brightest component on the fundus. Among segmentation methods, several approaches must be stressed: template-based algorithms, deformable models and morphological techniques. Most of algorithms based on mathematical morphology detect the OD by means of watershed transformation, generally through marker-controlled watershed, although each author proposes the use of different markers. Numerous OD segmentation methods, i.e., ODboundary detectors, have been reported in the literature. In general, the presented techniques can mainly be grouped into template-based methods, deformable models, and morphological algorithms. Different approaches have been proposed according to template-based methods: edge detection techniques followed by the Circular Hough Transform; pyramidal de-composition and Hausdorff-based template matching; color decorated templates; or a knn-regressor along with a circular template. Concerning deformable models, GVF-snake, ASM, and modified active contours, which exploit specific features of the optic disc anatomy or incorporate knowledgebased clustering and updating, have also been used to this purpose. The algorithm is fully automatic, so process is speeded up and user intervention is avoided making it completely transparent. Moreover, the method provides robustness in each processing step. First, it is independent of the database thanks to using PCA. Secondly, it employs the greyimage centroid as initial seed so that not only the pixel intensity is taken into account. Thirdly, it makes use of the stochastic watershed in order to avoid sub-segmentation problems related to classical watershed transformation. 2. Methodology 2.1 Glaucoma detection using cup-to-disc ratio Glaucoma is a chronic and irreversible neurodegenerative disease. Patients with early glaucoma do not usually have any visual signs or symptoms. Advanced glaucoma is associated with total blindness. An early detection of glaucoma is particularly significant since it allows timely treatment to prevent major visual field loss and prolongs the effective years of usable vision. The diagnosis of glaucoma can be done through measurement of CDR (cup-to-disc ratio). 4428
Fig. 1 a) Normal optic nerve head with small optic cup. b) Glaucomatous optic nerve head. The hole is larger, corresponding to the loss of nerve fibers. Currently, an important indicator of glaucoma is CDR, defined as the ratio of the vertical height of the optic cup to the vertical height of the optic disc. Optic nerve cupping progresses as the cup becomes larger (Figure 1(b)) in comparison to the optic disc as shown in Figure 1(a). A cup to disc ratio value that is greater than 0.65 is generally considered to be suspicious for glaucoma. 2.1.1 Methodology To calculate the vertical cup to disc ratio (CDR), the optic cup and disc first have to be segmented from the retinal images. Figure 2: Retina image processing framework for cup-to-disc ratio (CDR) detection in glaucoma analysis 4429
2.1.1.1 Region of Interest (ROI) Extract the optic disc, a region of interest around the optic disc must first be delineated, as the optic disc generally occupies less than 5% of the pixels in a typical fundus image. While the optic disc extraction can be performed on the entire image, localizing the ROI would help to reduce the computational cost as well as improve segmentation accuracy. In the images, regions are labelled by using the neighbourhood connecting pixels. The fundus image is subdivided into number of regions, and an approximate ROI Centre is selected based on the region containing the highest number of pre-selected pixels. 2.1.1.2 Optic Disc Segmentation The optic disc has to be segmented from the fundus images. The segmentation of object pixel is given thresholding value of 1 while a background pixel is given thresholding value of 0. The threshold value is using the extraction of optic disc boundary. In this paper, the optical disc boundary is localized using color planes as chose by the color analysis. The segmented optic disc uses edge detection. 2.1.1.3 Optic Disc Smoothing The optic disc is segmented and edge detection, but still the actual shape of optic disc boundary is not obtained as desired. Therefore, the ellipse fitting algorithm is used. 2.1.1.4 Optic Cup Segmentation The localization of the optic disc boundary and the cup segmentation provides to localize the optic cup boundary. The extraction of the cup from the optic disc boundary, image processing technique is used to segmentation of the optic cup. 2.1.1.5 Optic Disc Smoothing After the cup boundary localization, the ellipse fitting algorithms is used for accurate curvature. The CDR is consequentially obtained based on the height of detected cup and disc. 2.1.1.6 Ellipse Fitting Ellipse fitting algorithm can be used to smooth the optic cup and disc boundary. Ellipse fitting is usually based on the least square fitting algorithm which assumes that the best-fit curve of a given type is the curve that has the minimal sum of the deviations squared from, given data points. 2.2 Diabetic Retinopathy Detection Using Blood Vessel Segmentation in Retinal Images Diabetic Retinopathy (DR) is an eye disease which occurs due to diabetes. It damages the small blood vessels in the retina resulting in loss of vision. The blood vessel segmentation method includes the following processing stages: 1) Original fundus image pre-processing for gray-level homogenization and blood vessel enhancement. 2) Feature extraction for pixel numerical representation. 3) Application of a classifier to label the pixel as vessel or non-vessel, and 4) Post processing for filling pixel gaps in detected blood vessel and removing falsely detected isolated blood vessels. Input images are monochrome and obtained by extracting the green band from original RGB retinal images. The green channel provides the best vessel-background contrast of the RGBrepresentation, while the red channel is the brightest color channel and has low contrast, and the blue one offers poor dynamic range. Thus, blood containing elements in the retinal layer (such as vessels) are best represented and reach higher contrast in the green channel. 4430
2.2.1 Pre-Processing Color fundus images often show important lighting variations, poor contrast and noise. In order to reduce these imperfections and generate images more suitable for extracting the pixel features demanded in the classification step, a pre-processing is applied. Vessel Central Light Reflex Removal Since retinal blood vessels have lower reflectance when compared to other retinal surfaces, they appear darker than the background. Although the typical vessel cross-sectional gray-level profile can be approximated by a Gaussian shaped curve (inner vessel pixels are darker than the outermost ones), some blood vessels include a light streak (known as a light reflex) which runs down the central length of the blood vessel. To remove this brighter strip, the green plane of the image is filtered by applying a morphological opening using a three-pixel diameter disc, defined in a square grid by using eightconvexity, as structuring element. Disc diameter was fixed to the possible minimum value to reduce the risk of merging close vessels denotes the resultant image for future references. Background Homogenization Fundus images often contain background intensity variation due to non-uniform illumination. Consequently, background pixels may have different intensity for the same image and, although their gray-levels are usually higher than those of vessel pixels (in relation to green channel images), the intensity values of some background pixels is comparable to that of brighter vessel pixels. With the purpose of removing these background lightening variations, a shade-corrected image is accomplished from a background estimate. This image is the result of a filtering operation with a large arithmetic mean kernel. Vessel Enhancement The final pre-processing step consists on generating a new vessel-enhanced image (I VE ). Vessel enhancement is performed by estimating the complementary image of the homogenized image,, and subsequently applying the morphological Top-Hat transformation. (2.1) where ϒ is a morphological opening operation using a disc of eight pixels in radius. Thus, while bright retinal structures are removed (i.e., optic disc, possible presence of exudates or reflection artifacts), the darker structures remaining after the opening operation become enhanced (i.e., blood vessels, fovea, possible presence of micro aneurysms or haemorrhages). Fig.3 (a) Green channel of the original images (b) Homogenized images (c) Vessel-enhanced images. 4431
2.2.2 FEATURE EXTRACTION The aim of the feature extraction stage is pixel characterization by means of a feature vector, a pixel representation in terms of some quantifiable measurements which may be easily used in the classification stage to decide whether pixels belong to a real blood vessel or not. Gray-level-based features Features based on the differences between the gray-level in the candidate pixel and a statistical value representative of its surroundings. Moment invariants-based features Features based on moment invariants for describing small image regions formed by the gray-scale values of a window centered on the represented pixels. 2.2.3. Classification In the feature extraction stage, each pixel from a fundus image is characterized by a vector in a 7-D feature space (2.2) Now, a classification procedure assigns one of the classes (vessel) or (non-vessel) to each candidate pixel when its representation is known. Two classification stages can be distinguished: a design stage, in which the NN configuration is decided and the NN is trained, and an application stage, in which the trained NN is used to classify each pixel as vessel or non-vessel to obtain a vessel binary image. Neural Network Design A multilayer feed-forward network, consisting of an input layer, three hidden layers and an output layer. The input layer is composed by a number of neurons equal to the dimension of the feature vector (seven neurons). Regarding the hidden layers, several topologies with different numbers of neurons were tested. A number of three hidden layers, each containing 15 neurons, provided optimal NN configuration. The output layer contains a single neuron and is attached, as the remainder units, to a non- linear logistic sigmoid activation function, so its output ranges between 0 and 1. This choice was grounded on the fact of interpreting NN output as posterior probabilities. Neural Network Application At this stage, the trained NN is applied to an unseen fundus image to generate a binary image in which blood vessels are identified from retinal background pixels mathematical descriptions are individually passed through the NN. Since a logistic sigmoidal activation function was selected for the single neuron of the output layer, the NN decision determines a classification value between 0 and 1. Thus, a vessel probability map indicating the probability for the pixel to be part of a vessel is produced. In order to obtain a vessel binary segmentation, a thresholding scheme on the probability map is used to decide whether a particular pixel is part of a vessel or not. Therefore, the classification procedure assigns one of the classes (vessel) or (non-vessel) to each candidate pixel, depending on if its associated probability is greater than a threshold. Thus, a classification output image is obtained by associating classes and to the gray level values 255 and 0, respectively. 4432
Mathematically, where p(c1 F(x,y)) denotes the probability of a pixel (x,y) described by feature vector belong to class C 1. (2.3) to 2.2.4 Post processing Classifier performance is enhanced by the inclusion of a two- step post processing stage: the first step is aimed at filling pixel gaps in detected blood vessels, while the second step is aimed at removing falsely detected isolated vessel pixels. From visual inspection of the NN output, vessels may have a few gaps (i.e., pixels completely surrounded by vessel points, but not labelled as vessel pixels). To overcome this problem, an iterative filling operation is performed by considering that pixels with at least six neighbours classified as vessel points must also be vessel pixels. Besides, small isolated regions misclassified as blood vessel pixels are also observed. In order to remove these artifacts, the pixel area in each connected region is measured. In artifact removal, each region connected to an area below 25 is reclassified as non-vessel. 3. Result 3.1 Results & Analysis of Glaucoma Detection To evaluate the performance of the approach, we obtained 44 fundus images. There are 28 of the retinal images from normal patients, with no clinical signs of glaucoma, and 16 of the retinal images are from patients with glaucoma. At this point, the set of 44 test images are processed using the approach outlined earlier in order to obtain the CDR value, CDRAutomated. For the ground truth the optic cup and disc boundaries are assessed and annotated by a senior ophthalmologist based on the retinal fundus images, and the vertical CDR for each image, CDRClinic, was determined. The evaluation of the performance of our approach is divided into 3 parts, which are the performance of the optic disc boundary detection, the performance of the optic cup boundary detection, and the vertical cup-to-disc ratio (CDR). Figure 4: Error between CDRAutomated and CDRClinical 4433
Figure 5: Comparison between clinical CDR and proposed CDR 3.2 Resuts & Analysis of Diabetic Retinopathy Detection The retinal images from the DRIVE database and STARE database are used for evaluating the performance of the vessel segmentation method. The manually segmented vessels provided in both the databases are used as gold standard. Figure 6 and Figure 7 illustrates the result of vessel segmentation on one of the images in each database. The processing of each image including convolution and thresholding took about 30 seconds. a b c Figure 6 : Result of vessel segmentation on image from DRIVE database; (a) Input image; (b)manual segmentation by expert; (c)automatic Segmentation by the method 4434
a (c) b c Figure 7 : Result of vessel segmentation on image from STARE database; (a) Input image; (b)manual segmentation by expert; (c)automatic Segmentation by the method. 4. Conclusion From a fundus image, the implemented algorithm is able to automatically locate the OD. This method tries to make easier the early detection of diseases related to the fundus. Moreover, it has been validated on a public database obtaining promising results. The main advantage is the full automation of the algorithm. It does not require any intervention by clinicians, which releases necessary resources (specialists) and reduces the consultation time. For those reasons, its use in primary care is facilitated. The cup to disc ratio (CDR) is an important indicator of the risk of the presence of glaucoma in an individual. In this study, we have presented a method to calculate the CDR automatically from fundus images. The demonstrated effectiveness and robustness, together with its simplicity and fast implementation, make this proposed automated blood vessel segmentation method a suitable tool for being integrated into a complete pre-screening system for early DR detection. 5. References [1] D. Pascolini and S. P. Mariotti, Global estimates of visual impairment: 2010, Br. J. Ophthalmol., pp. 614 621, 2011. [2] World Health Organisation Media centre(april, 2011). Magnitude and Causes of Visual Impairment [Online]. Available:http://www.who.int/mediacentre/factsheets/ [3] P. Rojpongpan (March, 2009). Glaucoma is second leading cause of blindness[online]. Available: http://www.manager.co.th/qol/viewnews 4435
[4] S.Kavitha, S.Karthikeyan, Dr.K.Duraiswamy, Early Detection of Glaucoma in Retinal Images Using Cup to Disc Ratio, IEEE Second International conference on Computing, Communication and Networking Technologies, 2010, pp 1-5. [5] www.glaucoma.org. [6] Dougherty G., Johs on M. J., and Wiers M., Meas urement of retinal vas cular tortuos ity and its application to retina l pathologie s, Journal of Medical & Biological Engineering & Computing, vol. 48, no. 1, pp 87-95, 2010. 4436