The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Abstract

Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%. Keyword: Contour features Cup Fundus image Glaucoma Morphology Copyright © 2016 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Anindita Septiarini, Faculty of Computer Science and Information Technology, Universitas Mulawarman, Samarinda-75123, Indonesia. Email: anindita@unmul.ac.id INTRODUCTIONGlaucoma is an eye disease that can cause vision problems. This disease is the second cause of blindness in the world [1], therefore the detection of glaucoma is required. The detection of glaucoma by an expert carried out by observing the structure of the retina using a fundus image, to determine the eyes of the patient are normal and glaucoma. Currently, research on automatic glaucoma detection techniques have been developed. Most of the research using fundus images as input data and several feature extraction methods have been applied.Several previous researches related to automatic glaucoma detection generated by applying nonmorphological feature extraction method (image-based features). The feature referred to color, shape or texture on the optic nerve head (ONH), retinal nerve fiber layer (RNFL) or blood vessel as part of the structure of the retina [2]. These parts undergo changes in patients with glaucoma [3]. ONH (also called disc) is the part area with round shape. Inside of the disc, there is a smaller area with round shape called a cup and a neuroretinal rim which is an area that lies between the disc and the cup. RNFL is the area outside of the disk where the structure of thickness of the RNFL is the one which distinguishes whether a patient's eye is normal or glaucoma. The illustration of ONH and RNFL structure are shown in Figure 1.In the features extraction of the ONH applied the process of blood vessel inpainting as the previous phase in order to remove or blurring the blood vessel that presences as noise. This process is required the detection to determine the presence of the blood vessel. Several methods have been proposed for the detection of the blood vessel. Thresholding with the low pixel values in the image followed by median filtering of the size 41x41 [4], using threshold probing [5] and normalized mathematical morphology (used to enhance the vessel...

Optic Disc and Macula Localization from Retinal Optical Coherence Tomography and Fundus Image

“…The noise in OCT images have the form of dots or pixels with different intensity. This study used the first derivative of Gaussian filter to reduce the noise on OCT image with the formula below: Whereas, the vector gradient and the amplitude can be calculated with [9] :…”

“…The result of matrix feature will be used as an input for training and testing process. The training and testing process in this study using SVM method [9]. The research was performed by establishing an automatic diagnostic system to detect macule using fluorescein image.…”

Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images

“…Initially, thresholding based on the green channel of RGB with a high threshold is applied in to identify the approximate ONH center. We chose the green channel because the center of the ONH is more easily identified in this channel [ 13 23 ]. Subsequently, a border mask must be formed to overcome the noise that may occur at the border of the retina.…”

“…The image enhancement process aims to improve the result of the subsequent process. In this work, we remove the blood vessels because they cover the area of the ONH by closing operation as in [ 8 23 ]. Subsequently, we convert the result of the previous step into a grayscale image followed by histogram equalization to increase the quality of the image.…”

“…In addition to texture, shape features have been analyzed for glaucoma detection [ 21 22 ]. The implementation of shape feature analysis was proposed in [ 23 ] based on the cup and SVM contours.…”