SYSTEM BASED ON THE NEURAL NETWORK FOR THE DIAGNOSIS OF DIABETIC RETINOPATHY

The legal cause of blindness for the workingage population in western countries is Diabetic Retinopathy a complication of diabetes mellitus is a severe and widespread eye disease. Digital color fundus images are becoming increasingly important for the diagnosis of Diabetic Retinopathy. In order to facilitate and improve diagnosis in different ways, this fact opens the possibility of applying image processing techniques . Microaneurysms is the earliest sign of DR, therefore an algorithm able to automatically detect the microaneurysms in fundus image captured. Since microaneurysms is a necessary preprocessing step for a correct diagnosis. Some methods that address this problem can be found in the literature but they have some drawbacks like accuracy or speed. The aim of this thesis is to develop and test a new method for detecting the microaneurysms in retina images. To do so preprocessing, gray level 2D feature based vessel extraction is done using neural network by using extra neurons which is evaluated on DRIVE database which is superior than rulebased methods. To identify microaneurysms in an image morphological opening and image enhancement is performed. The complete algorithm is developed by using a MATLAB implementation and the diagnosis in an image can be estimated with the better accuracy and in shorter time than previous techniques. Key words— Diabetic retinopathy, feature extraction, fundus photograps, microaneurysms, Vessel Segmentation. I.INTRODUCTION Diabetic Retinopathy (DR) is the most common diabetic eye disease and is the leading cause of blindness in a persons with diabetes within the age group 20 to 74 years in developed countries [1]. It is caused by the changes in the blood vessel of retina. It is provoked by diabetes-mellitus complications and, although diabetes affection does not necessarily involve vision impairment. About 2% of the patients affected by this disorder are blind,10% undergo vision degradation after 15 years of diabetes [2], [3] as a consequence of DR complications The estimated prevalence of diabetes for all age groups worldwide was 2.8% in 2000 and 4.4% in 2030, meaning that the total number of diabetes patients is forecasted to rise from 171 million in 2000 to 366 million in 2030 [4]. DR also becomes a great economic problem for Administrations since, only in U.S. cost of ophthalmic chronic complications caused by diabetes exceeded 1 billion dollars in 2007 [5]. Although the diabetic retinopathy is not curable and also DR patients perceive no symptoms until visual loss develops, usually in the later disease stages, when the treatment is less effective. But if the treatment is received in time patients can be prevented to become affected from this condition or atleast the progression of DR can be slowed down. Thus, mass screening of the patients suffering from the diabeties is highly desired, but manual grading is slow and resource demanding. Therefore more effort are being taken to design authentic computer aided screening system based on color fundus images. The promising result reported by Antal et al.[6] indicate that the automatic DR screening systems are getting closer to be used in clinical practices. These types of automated systems have the potential to reduce the workload for screening ophthalmologists while maintaining a high sensitivity (i.e., above 90%) for the detection of patients with DR. Furthermore, itwould also result in economic benefits for public Health Systems, since costeffective treatments associated to early illness detection lead to remarkable cost savings [7]. In this paper, a new methodology for blood vessel detection presented by Marin et al. [9] is optimized for the MA detection. It is based on pixel classification using a 2-D feature vector extracted from preprocessed retinal images and given as input to a neural network. Classification results (real values between 0 and 1) are thresholded to classify each pixel into two classes: vessel and nonvessel. Finally, a postprocessing fills pixel gaps in detected blood vessels and removes falsely-detected isolated vessel pixels. Despite its simplicity, the high accuracy achieved by this method in blood vessel detection is comparable to that reported by the most accurate methods in literature. Moreover, it offers a better behavior against images of different conditions. This fact is especially relevant if we keep in mind that the main aim of implementing a vessel segmentation algorithm is its integration in systems for automated detection of eye diseases. This kind of systems should require no user interaction and, therefore, be field of retinal imaging, this involves a huge challenge, since large variability is observed in the image acquisition process and a natural variation is reported in the appearance of the retina. Applications are being developed in which a computer interprets an image to aid a physician in detecting possibly subtle abnormalities. A spell-checker indicates words or grammar it suspects to be incorrect. This may or may not be the case and the human operator, the writer in this case, either accepts or rejects the machine’s suggestions. A similar process can be used for medical image analysis. The computer indicates places in the image that require extra attention from the physician because they could be abnormal. These technologies are called Computer Aided Diagnosis (CAD). Other emerging CAD applications are the automatic detection of polyps in the large intestine and automatic lung nodule detection. This thesis describes components of an automatic system which can aid in the detection of diabetic retinopathy.This is an eye disease and a common complication of diabetes that can cause blindness and vision loss if left undiagnosed at an early stage. As the number of people afflicted with diabetes increases worldwide, the need for automated detection methods of diabetic retinopathy will increase as well. To automatically detect International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra. com Volume 2, Issue 4 (July-Aug 2014), PP. 76-80 77 | P a g e diabetic retinopathy, a computer should interpret and analyze digital images of the retina.