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    • International Journal of Advanced Research in Engineering RESEARCH IN ENGINEERING INTERNATIONAL JOURNAL OF ADVANCED and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 2, February (2014), pp. 95-100 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 4.1710 (Calculated by GISI) www.jifactor.com IJARET ©IAEME A REVIEW ON DIFFERENT GLAUCOMA DETECTION METHODS Saja Usman1, Dimple Shajahan2 1 2 (Computer Science and Engineering, TKM College of Engineering, Kollam, India) (Computer Science and Engineering, TKM College of Engineering, Kollam, India) ABSTRACT Glaucoma is one of the leading cause of blindness worldwide. It is due to the decrease in intra ocular pressure within the eyes. The detection and diagnosis of glaucoma is very important. There are manual and automatic detection methods available. In this paper a survey is conducted on different glaucoma detection methods in image processing such as Scanning Laser Polarimetry, optical coherence tomography, mfreg, Wavelet Fourier transform etc and also their advantages and disadvantages. This paper also discusses a new method to classify the glaucomatous images which is the proposed method. Keywords- Glaucoma, Cup to disc ratio, Wavelet Transform, Classifiers, Artificial Neural Network, Segmentation. I. INTRODUCTION Glaucoma is one of the main reason of blindness worldwide. This is caused due to the increase in intra ocular pressure within the eyes. The intra ocular pressure increases due to the malfunction or malformation of the drainage system of the eye. Aqueous humor- a clear fluid flow in and out of the chamber of the eye. This fluid nourishes the nearby tissues. Aqueous humor controls the intra ocular pressure of the eye. The pressure within the eye is maintained by providing a small amount of aqueous humor while the same amount of fluid flows out of the eye through a drainage system. Pressure within the eye increases because of the insufficient fluid flow between the iris and the cornea. The increased intra ocular pressure in the eye damages the optical nerve through which retina sends light to the brain where they are recognized as images and make vision possible. The major reason for glaucoma is the increased pressure variation in the eye. One technique to asses patients who are suffering from glaucoma is to analyse the ultrasound images of the eye in order to detect the changes that reduces the flow of fluid out of the eye through the drainage system. Usually the analysis of this images are done manually. The features that are 95
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME analysed by the clinicians while testing the eye are the sclera- a dense fibrous opaque white outer coat enclosing the eye ball, sclera spur- a minute triangular area in a meridional section of the sclera tissue with its base along the inner surface of the sclera; the anterior chamber, the region surrounded by the latter surface of the cornea and the central part of the lens; and, the trabecular-iris recess, the top point between the sclera region and the iris. Manual analysis of the eye is time consuming and the accuracy of the parameter measurements also varies with different clinicians. To overcome this problems with the manual analysis, the objective of this paper is to introduce a method to automatically analyse the ultrasound images of the eye. Automatic analysis is much more effective than manual analysis. Since glaucoma is the second leading a use to blindness its very important to detect it and diagonise. The remaining of this paper is organized as follows. Section II discusses about the different methods and techniques by which glaucoma can be detected. Section III gives a brief explanation about the proposed system, and section IV concludes the paper. II. TECHNIQUES INVOLVED Glaucoma is a disease characterized by degeneration of optic nerves(optic disc). So the fall in blood flow to the optic nerve give to the visual field defects associated with glaucoma. Drug therapy to control the elevated intraocular pressure and serial evaluation of the optical nerves are the principal method of curing the disease. Standard methods of evaluation of the optic nerve using ophthalmology or stereo photography or evaluation of visual fields. There has been importance in rising more objective, reproducible techniques to document optic nerve damage as well as to perceive early on changes in the optic nerve and retinal nerve fiber layer (RNFL) before the development of eternal visual field deficits. Particularly, evaluating changes in the thickness of the retinal nerve fiber layer have been investigated as a method to make a diagnosis and observe glaucoma. In addition, there has been importance in measuring ocular blood flow as a diagnostic and a supervision tool for glaucoma. This section briefly describes some of the techniques that are used for the detection of glaucoma. 1.1. Confocal Scanning Laser Ophthalmology In [1] Alexandrescus Dascular AM introduced a Confocal scanning laser ophthalmoscopy(CSLO), a laser based image gaining which is proposed to improve the quality of the examination compared to ordinary ophthalmologic examination. A laser id scanned crossways the retina along with a detector system. Once a single spot on the retina is illuminated at any time, ensuing in a high-contrast image of great reproducibility that can be used to estimate the width of the RNFL. In addition, this technique does not need maximal mydriasis, which may be a problem in patients having glaucoma. The Heidelberg Retinal Tomography is possibly the most common example of this technology. 1.2. Scanning Laser Polarimetry In [2] Ferreri F,Aragona P introduced the SLP. RNFL is birefringent, which causes a change in the state of divergence of a laser beam as it passes. It uses a 780-nm diode to illuminate optic nerve. The polarization state of the light emerging from the eye is then evaluated and linked with RNFL thickness. Unlike CSLO, scanning laser polarimetry (SLP) can unswervingly measure the thickness of the RNFL. GDx® is an ordinary example of a scanning laser polarimeter. GDx® contain a normative database and statistical software package to permit comparison to age-matched normal subjects of the same racial origin. The advantages of this system are that images can be obtained without pupil dilation, and evaluation can be done roughly in 10 minutes. Modern 96
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME instruments have added improved and erratic corneal compensation technology to account for corneal polarization. 1.3. Optical Coherence Tomography In[3] Optical coherence tomography (OCT) uses near-infrared light to provide direct crosssectional measurement of the RNFL. The principles employed are alike to those used in B-mode ultrasound except light, not sound, is used to create the 2-dimensional images. The light source can be directed into the eye through a conservative slit-lamp biomicroscope and focused on the retina through a distinctive 78-diopter lens. This system requires dilation of the patient’s pupil. OCT® is an example of this technology. 1.4. mfREG J. M. Miquel-Jimenez[4] et al proposed the Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram. Existing clinical analysis of the multifocal electroretinography (mfERG) recording for detecting glaucoma is based on standard signal morphology, that measures the amplitudes and latencies. This analysis is not sensitive enough for detection of minute changes in the multifocal electroretinogram signals. Here an another method for the detection of open angle glaucoma based on the categorization of global flash mfERG signals is given. The digital signal processing technique is based on wavelets for the detection of advancedstage glaucoma. Two markers were obtained from the recorded signals by applying the discrete wavelet transform, which help discriminate healthy from glaucomatous signals. 1.5. Pulsatile Ocular Blood Flow The pulsatile disparity in ocular pressure is due to the flow of blood into the eye during cardiac systole. Pulsatile ocular blood flow can detect by the permanent monitoring of intraocular pressure. The detected pressure pulse can be changed into a volume measurement using the identified relationship between ocular pressure and ocular volume. Pulsatile blood flow is principally determined by the choroidal vessels, mainly applicable to patients with glaucoma, since the optic nerve is supplied in large part by choroidal circulation. The optical coherence tomography and multifocal electro retinograph (mfERG) are wellknown methods working in order to analyze functional abnormality of the eye especially glaucoma. 1.6. Computer Based Diagnosis of Glaucoma using Digital Fundus Images. Archana Nandibewoor[5] introduced the Computer Based Diagnosis of Glaucoma using Digital Fundus Images. Objective of this paper is to map the person’s eye color image with database having images of normal person as well as images of people with glaucoma. Images having different color variation inside the eye is compared by using images taken by high definition laser camera. These are called as fundus images. MATLAB software tool is used to extract features from these fundus images. We can know whether a person is suffering from glaucoma by measuring the color pixels at the affected areas. To identify whether the person is suffering from Glaucoma, a test is made using the image of a normal person which is kept as reference (say zero) and then compared with the clinical observations of the person’s image. Simonthomas.S, Thulasi.N[6] presented a method for glaucoma detection using an Haralick Texture Features from digital fundus images. These features were chosen because they can be made invariant to translations and rotations, because they describe more spontaneous aspects of the images (e.g. coarse versus smooth, directionality of the pattern, image complexity, etc.) using statistics of the gray-level co-occurrence matrix. For each image. K Nearest Neighbors (KNN) classifiers are used to perform supervised classification. Haralick Texture Features has Database and classification parts, in Database the image has been loaded and Gray Level Co-occurrence Matrix (GLCM) and thirteen 97
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME haralick features are combined to extract the image features, performs better than the other classifiers and correctly identifies the glaucoma images. CDSS[7] are used in ophthalmology to generate a decision support system that identify disease pathology in human eyes. In CDSS two types of features are extracted from the images, structural and texture features. The disk area, rim area, cup to disc ratio and topographical features are the extracted structural features. Glaucoma can be automatically diagnosed by calculating the cup to disc ratio. The CDR (Cup-to-Disc Ratio) is defined as the ratio of the vertical cup height to the vertical disc height. A CDR value that is greater than 0.65 indicates high glaucoma risk. The glaucoma diagnosis can be improved by the enhancement of optic cup to disc ratio[8]. M.Balasubramanian [9] proposed a method known as Proper orthogonal decomposition (POD) which is a technique that uses structural features to identify glaucomatous progression. In POD the pixel-level information is used to estimate significant changes across samples that are location or region specific. Structural features are location or region specific, so the detection of the disease is limited to that region only. The measurement of texture features is roughly defined as the spatial variation of pixel intensity (gray-scale values) across the image. Textural features are, thus, not bound to specific locations on the image techniques, including spectral techniques, are available to determine texture features. 1.7. Wavelet Fourier Analysis The texture-based techniques have been proven successful but its still a challenge to generate features that retrieve generalized structural and textural features from retinal images. To overcome the generalization of features wavelet transforms[10] in image processing are used to extract the texture features from images.. In WT, the image is represented in terms of the frequency of content of local regions over a range of scales. This representation helps the analysis of image features, which are independent in size and can often be characterized by their frequency domain properties. The use of WFA for the categorization of neuroanatomic distraction in glaucoma has achieved substantial success. WFA is used as an exact model to analyze and parameterize the temporal, superior nasal, inferior, and temporal (TSNIT) shapes. Two types of wavelet transforms are used, discrete wavelet transforms and continuous wavelet transforms in image processing. In this method, discrete wavelet transform (DWT) using a fourth-order symlets wavelet is used to extract features and analyze discontinuities and rapid changes contained in signals. DWT is a multiscale analysis method where analysis can be performed on a range of scales. Each level of the transformation provides an analysis of the source image at a different resolution, resulting in its independent rough calculation and detailed coefficients. In the WFA, the fast Fourier transform (FFT) is applied to the detailed coefficients. The resultant Fourier amplitudes are combined with the normalized approximation coefficients of the DWT to create a set of features. Sumeet Dua, U. Rajendra Acharya, Pradeep Chowriappa, S. Vinitha Sree introduced another method in[10] to classify normal eye images and glaucoma affected eye images based on the distribution of average texture features obtain from three prominent wavelet families. Their objective is to evaluate and select important features for improved specificity and sensitivity of glaucomatous image classification. Quantitatively examine the effectiveness of different wavelet filters on a set of curated glaucomatous images by employing the standard 2-D-DWT. Use three well-known wavelet filters, the daubechies (db3), the symlets (sym3), and the biorthogonal (bio3.3, bio3.5, and bio3.7) filters. Then calculate the averages of the detailed horizontal and vertical coefficients and wavelet energy signature from the detailed vertical coefficients and subject the extracted features to abundant of feature ranking and feature selection schemes to determine the best combination of features to maximize interclass similarity and assist in the union of classifiers, such as the support vector 98
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME machine (SVM), sequential minimal optimization (SMO), random forest, and na¨ıve Bayes techniques. III. PROPOSED SYSTEM Our objective is to classify the images of retina as normal and glaucoma affected and the segment the affected part from the glaucomatous images. The energy features extracted from the images using the three different wavelet families are given to the artificial neural network for classification. Apply any clustering models to the affected images to segment the detected part. The Modules includes are the following: i. ii. iii. iv. Discrete Wavelet Transforms Energy feature Extraction Artificial neural network training and classification Segmentation Overview • The system is designed for automatic retina glaucomatous image classification and segmentation of the affected part. • The image classification module includes the feature extraction by using three prominent wavelet families and classification by artificial neural network. • The DWT will be used to decompose the image into four subbands. The energy features are extracted from the high frequency coefficients and these subbands are contained the detailed coefficients. • The energy features obtained from the images are given to the ANN for classification. The trained network will classify the images into either normal or glaucomatous. • In case of glaucomatous images a clustering model for segmentation is applied to detect the affected parts. IV. CONCLUSION Glaucoma is the second leading cause of blindness in the world. Hence its detection and diagnosis are very essential. Different manual and automatic glaucoma detection methods are discussed here. A new method is introduced to classify the images into glaucomatous and normal by ANN and to detect the affected parts. Overview of the proposed system also discussed. V. REFERENCES [1] [2] [3] [4] Alexandrescus C, Dascular AM “Confocal Scanning Laser Ophthalmoscopy in Glaucoma Diagnosis and Management”,J Med Life 2010 Jul-Sep;3(3):229-34. Ferreri F,Aragona P “Scanning LaserPolarimetry and Confocal Scanning Laser Ophthalmology:Technical notes on their use in Glaucoma” Prog Brain Res 2008;173:125-38. doi: 10.1016/S0079-6123(08)01109-6. K. R. Sung et al., “Imaging of the retinal nerve fiber layer with spectral domain optical coherence tomography for glaucoma diagnosis,” Br. J. Ophthalmol., 2010. J. M. Miquel-Jimenez et al., “Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram,” Med. Eng. Phys., vol. 32, pp. 617–622, 2010. 99
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 95-100, © IAEME [5] Archana Nandibewoor S B Kulkarni Sridevi Byahatti Ravindra Hegadi” Computer Based Diagnosis of Glaucoma using Digital Fundus Images”, Proceedings of the World Congress on Engineering 2013 Vol III, WCE 2013, July 3 - 5, 2013, London, U.K. [6] Simonthomas.S, Thulasi.N Automated Diagnosis of Glaucoma using Haralick Texture Features IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 1 (Sep. - Oct. 2013), PP 12-17. [7] S.Weiss, C.A. Kulikowski, and A. Safir, “Glaucoma consultation by computer,” Comp. Biol. Med, vol. 8, pp. 24–40, 1978. [8] A.Murthi and M.Madheswaran, ”Enhancement of optic cup to disc ratio detection in glaucoma diagnosis,” International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA978-1-4577- 15839/12/$26.00 © 2012 IEEE. [9] M. Balasubramanian et al., “Clinical evaluation of the proper orthogonal decomposition framework for detecting glaucomatous changes in human subjects,” Invest. Ophthalmol. Vis. Sci., vol. 51, pp. 264–271, 2010. [10] E. A. Essock,Y. Zheng, and P.Gunvant, “Analysis of GDx-VCC polarimetry data by waveletFourier analysis across glaucoma stages,” Invest. Ophthalmol. Vis. Sci., vol. 46, pp. 2838–2847, Aug. 2005. [11] Sumeet Dua, U. Rajendra Acharya, Pradeep Chowriappa, S. Vinitha Sree”, Wavelet-Based Energy Features for Glaucomatous Image Classification” IEEE transaction on information technology in biomedicine, vol 16, No.1, January 2012. [12] Nataraj A.Vijapur, Dr.Rekha Mudhol and Dr.R.Shrinivasa Rao Kunte, “An Approach for Detection of Primary Open Angle Glaucoma”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 6, 2013, pp. 185 - 194, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 100