International Journal of Engineering Research & Science (IJOER) ISSN: [2395-6992] [Vol-2, Issue-12, December- 2016] Page | 106 Complex Texture Features for Glaucomatous Image classification System using Fundus Images Srinivasan C 1 , Dr Suneel Dubey 2 , Dr Ganeshbabu TR 3 1 Research scholar, 2 Professor & HOD, 3 Professor 1,2 Department of CSE, Maharishi University of Information Technology, Lucknow, India 3 Department of ECE, Muthayammal Engineering College, Rasipuram, India Abstract— In this paper, an efficient approach for glaucomatous image classification system using fundus images is proposed. The main aim of this study is to detect glaucoma accurately in order to reduce the visual loss and impairment. The proposed system uses two important texture features; Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) in an efficient manner. These texture features are extracted not only from the fundus image but also the optical density image obtained from the fundus image. Before extracting features, region of interest is obtained from the Green channel of the fundus image as it has high contrast than other two colour components. Support Vector Machine (SVM) classifier is used for the classification of fundus image into normal or abnormal based on the extracted features. Results show that the proposed system provides promising results with 100% sensitivity and 99% specificity. Keywords— Glaucoma, fundus image, optical density, GLCM, LBP, SVM classifier. I. INTRODUCTION Blindness is the lack of visual perception due to neurological or physiological factors. The major causes of blindness are glaucoma, cataract, age-related macular degeneration, and corneal opacity. Among them, glaucoma is one of the irreversible blindness. An extensive literature survey has been done and some of them are outlined here. An approach to detect glaucoma using Cup to Disc Ratio (CDR) and ISNT rule is discussed in [1]. At first, Region Of Interest (ROI) is extracted and CDR is measured by segmenting optic disc and cup region. The blood vessels in the optic disc area are tracked using hessian based vessel enhancement technique to compute ISNT ratio. Then, SVM classifier is used for classification. Deep convolutional neural network based glaucoma detection is discussed in [2]. It consists of six layers; four convolutional layers and two fully connected layers. Overlapping-pooling layers and response-normalization layers are adopted to reduce the overfitting problem. A review of various automated techniques for glaucoma diagnosis is discussed in [3] including active contour model, super pixel clustering, vessel bend, simple linear iterative clustering, and pallor information. Four different features such as CDR, horizontal to vertical CDR, cup to disc area ratio and rim to disc area ratio are used for glaucoma diagnosis in [4]. Optic disc segmentation is done with geodesic active contour model and cup segmentation is based on pallor appearance in the optic disc region. Finally, naïve Bayes, K- nearest neighbour and SVM classifiers are used for classification. Image based features along with segmentation based features are used for glaucoma diagnosis in [5]. Illumination correction is done before extracting features. Optic disc region is detected using Hough transform and template matching is used for ROI extraction. Finally, SVM classifier is adopted for classification. Texture and higher order spectral features are employed in [6] for glaucoma detection. Histogram equalization is applied to enhance the contrast before feature extraction. Texture features are extracted using GLCM. Random forest, SVM, and sequential minimal optimization algorithms are used for classification. Optic disc segmentation using morphological operations and hybrid level-set methodology for glaucoma diagnosis is described in [7]. Optic disc is segmented with the help of SVM classifier to detect blood vessels and bending points on the circum linear vessels. Glaucoma is detected by using cup to-disc area ratio and vertical cup-to-disc ratio. The segmentation of optic disc is done by LBP in [8]. LBP are obtained from the red channel of the fundus image after improve the quality of the image by histogram equalization. Finally, the artifacts are removed by morphological operations and filtering the LBP output. CDR and ISNT features are used for the diagnosis of glaucoma in [9]. It uses various morphological operations for optic disc and cup segmentation and also for blood vessel extraction. The assessment of glaucoma using optic disc and optic cup segmentation from monocular color retinal images is presented in [10]. In multidimensional feature space, the information of local image is integrated around each point of interest for OD segmentation. For cup segmentation, the region of support concept is used to detect vessel bends. Then, the right scale is