EARLY DETECTION OF MACULAR EDEMA IN FUNDUS IMAGE DATASETS USING NEURAL NETWORK

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. Dme occurs when fluid and protein deposits collect on or under the macula of the eye. These leakages cause the macula to thicken and swell, progressively distorts a persons vision. So in this article detection of exudates and its proximity towards macula determines and thereby its severity level. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature. A two-stage methodology for the detection and classification of dme severity from color fundus images is proposed. Dme detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from dme images. I.INTRODUCTION A.Macular Edema According to the 2011 National Diabetes Fact Sheet, 8.3 % of the populations in United States have diabetes [1] and today, nearly 1.2 million people in Malaysia have been diagnosed with diabetes in which half of them are unaware that they have the disease [2]. Prolonged suffering with diabetes may lead to diabetic retinopathy which is characterized by the damage of blood vessels of the retina. Diabetic macular edema is a complication of diabetic retinopathy and is often the true cause of visual loss and blindness [3]. Diabetic macular edema is diagnosed when interstitial fluid leaks from blood vessels within the macula region. The leakage is caused by the break-down of endothelial tight junctions in microaneurysms or retinal vessels. Lipid deposits accumulated in the retina resulting from the leakage is called exudates. Clinically, the exudates appear as well-defined, yellowish white intraretinal deposits on digital fundus image. Macula contains high concentration of photoreceptors responsible for shape and colour vision and it appears as dark area as seen in Fig. 1. The centre of macula is called fovea. The important step prior to exudates extraction is to remove optic disc on the image because exudates and optic disc has similar brightness. Optic disc is the entrance of blood vessel. Original fundus image of a patient diagnosed with stage 2 diabetic macular edema. Fovea is located at the center of the macula. (dotted arrow) and optic nerve. The occlusion of optic disc will not affect the detection process as there are usually no exudates found around the optic disc. A number of techniques to extract optic disc can be found in the literature [4]. In order to extract exudates, thresholding technique is the simplest technique but any low-contrast or uneven illumination image will affect the selection of threshold value for the exudates. A new unsupervised method using mixture model based on selecting dynamic threshold for exudates is proposed in [5], obtaining a sensitivity of 90.2%. In [6], candidate exudates regions were obtained by making use of the local properties of exudates and the combined global and adaptive histogram thresholding methods. The candidate regions were then classified as exudates or non-exudates by machine learning approach using statistical classifiers. The general limitation of using classifiers includes the intensive computing power required for training and classification. Besides, training size that is sufficient enough to represent the great properties variability of exudates has to be used. Morphological approach is recently employed because it requires less computational time and less computing power. In work done in [7], morphological operators were used in combination with sets of known shapes to give optimal contrast between the exudates and the background. In [8], four brightness properties on the exudates were used as input parameters to segmentation using Fuzzy C-means clustering that later fined tune with the morphological technique. This method was tested on low-contrast images and yields a sensitivity of 87.2 % and specificity of 99.2 %. In [9], a macular grid was placed centered on the fovea to provide the ophthalmologist with the distance information between the detected exudates and the macula. An algorithm for the localization of the macular has been presented in [10] by performing a search through a correlation procedure. In this paper, we intend to take one step further by locating the fovea location to develop an automated system that classifies diabetic macular edema into different severity stages. Since we aim at the realization of an automated system to grade severity stages, we would like to use it in combination with the exudates detection International Journal of Advanced Science and Engineering Research www.ijaser. in Volume: 2, Issue: 1, April 2017 ISSN: 2455-9288 590 Copyright © 2017 by the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited algorithm adopted from [11] because of its simplicity and its comparable results with known research works. Fig.2 shows the flow chart of the entire system in which our proposed classification method is highlighted with dotted lines. II.IMAGE PROCESSING In image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. Bag-of-words-based image classification approaches mostly rely on low level local shape features. However, it has been shown that combining multiple cues such as color, texture, or shape is a challenging and promising task which can improve the classification accuracy. Most of the state-of-the-art feature fusion methods usually aim to weight the cues without considering their statistical dependence in the application at hand. A.Anatomical Struceture of Human Eye Fig. 1. Color fundus image with anatomical structures and lesions annotated The human eye has been called the most complex organ in our body. It's amazing that something so small can have so many working parts. Working of Human Eye: In a number of ways, the human eye works much like a digital camera:  Light is focused primarily by the cornea the clear front surface of the eye, which acts like a camera lens.  The iris of the eye as shown in the Figure 1.1 functions like the diaphragm of a camera, controlling the amount of light reaching the back of the eye by automatically adjusting the size of the pupil.  The eye's crystalline lens is located directly behind the pupil and further focuses light. Through a normal process called accommodation, this lens helps the eye automatically focus on near and approaching objects, like an autofocus camera lens.  Light focused by the cornea and crystalline lens then reaches the retina the light sensitive inner lining of the back of the eye. The retina acts like an electronic image sensor of a digital camera, also converting optical images into electronic signals. The optic nerve then transmits these signals to the visual cortex the part of the brain that controls our sense of sight. International Journal of Advanced Science and Engineering Research www.ijaser. in Volume: 2, Issue: 1, April 2017 ISSN: 2455-9288 591 Copyright © 2017 by the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited B.Purpose of Image Processing The purpose of image processing is divided into 5 groups. They are: 1. Visualization Observe the objects that are not visible. 2. Image sharpening and restoration To create a better image. 3. Image retrieval Seek for the image of interest. 4. Measurement of pattern – Measures various objects in an image. 5. Image Recognition – Distinguish the objects in an image.