retinal desease

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Neural Network Based Retinal Image Analysis J.David Rekha Krishnan Sukesh Kumar. A College of Engineering Trivandrum,Kerala,India [email protected] Abstract  Diabetic-retinopathy contributes to serious health  problem in many parts of the world. With the motivation of the needs of the medical community  system for early screening of diabetics and other diseases a computer aided diagnosis system is  proposed. This work is aimed to develop an automated  system to analyze the retinal images for important  features of diabetic retinopathy using image  processing techniques and an image classifier based on artificial neural network which classify the images according to the disease conditions. The consistent identifying and quantifying of changes in blood vessels and different findings such as exudates in the retina over time can be used for the early detection of diabetic retinopathy. The algorithm has been tested on an image data base and the results are presented. Vascular network, optic disc and lesions like exudates are identified. A neural network classifier is developed and a comparative study on the performance is also  presented  . 1. Introduction Diabetic retinopathy is a micro vascular complication that may occur in patients with diabetes. The occurrence of diabetic retinopathy will result in the disturbance of visual capability and can eventually leads to  blindness. The longer a person has untreated diabetes, the higher his chance of developing diabetic retinopathy. Along with diabetes, high blood sugar levels in long periods can affect small vessels in the retina. Diabetic retinopathy becomes symptomatic in its later stage.  In the first stage, diabetic patients may not be aware of having infected by the disease [1]. Early detection of diabetic retinopathy plays a major role in the success of such disease treatment, so that the worse case can be anticipated. Diagnosis of diabetic retinopathy is usually conducted by the Ophthalmologist by employing retinal images of patients. By using a fundus camera, an ophthalmologist can obtain retinal images from  patients to be diagnosed. From the image symptoms will be identified manually by an ophthalmologist, therefore the more patients to be diagnosed; the more time will be needed. A computerized screening system can be used for fully automated mass screening [2]. Such systems screen a large number of retinal images and identify abnormal images, which are then further examined by an ophthalmologist. This would save a significant amount of workload and time for ophthalmologists, allowing them to concentrate their resources on surgery and treatment.  Normal structures of retina are the optic disk, macula, and blood vessels. The characteristic features of diabetic retinopathy are microaneurysms, hemorrhages and exudates. This work describes image analysis methods for the automatic recognition of retinal components and pathologies like exudates [3]. From the set of parameters like vessel ratio, ratio of exudates area to the total area of the images are distributed into different groups like normal, severe, mild, abnormal etc. Neural network can be used effectively in data classificat ion [4]. 2. Proposed System The fundus photographs were taken with a fundus camera during mass screening. These photographs were then scanned by a flat-bed scanner and saved as image files. The image files were then analyzed using the algorithms described in the following section: The  block diagram of the proposed system is shown in figure (1). 2008 Congress on Image and Signal Processing 978-0-7695-3119-9/08 $25.00 © 2008 IEEE DOI 10.1109/CISP.2 008.666 49 2008 Congress on Image and Signal Processing 978-0-7695-3119-9/08 $25.00 © 2008 IEEE DOI 10.1109/CISP.2 008.666 49 Authorized licensed use limited to: Jawaharlal Nehru Technological Univer sity. Downloaded on July 17, 2009 at 00:59 from IEEE Xplore. Restricti ons apply.

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