DocumentCode :
3580653
Title :
Neural Network Based Method for the Diagnosis of Diabetic Retinopathy
Author :
Gadriye, Dipika ; Khandale, Gopichand
Author_Institution :
Dept. of Electron. Eng., Wainganga Coll. of Eng., Nagpur, India
fYear :
2014
Firstpage :
1073
Lastpage :
1077
Abstract :
Diabetic Retinopathy is a severe and wide-spread eye disease, it is the main cause of blindness for the working age population in western countries. For the diagnosis of Diabetic Retinopathy, digital color fundus images are becoming increasingly important. This fact opens the possibility of applying image processing techniques in order to facilitate and improve diagnosis in different ways. As micro aneurysms are earliest sign of DR, therefore an algorithm able to automatically detect the micro aneurysms in fundus image captured 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. This system aims to develop and test a new method for detecting the micro aneurysms in retina images. Gray level 2D feature based vessel extraction is done using neural network to do preprocessing. The method is evaluated on DRIVE database and prove to be superior than rule based methods. To identify micro aneurysms in an image morphological opening and image enhancement is performed. A MATLAB implementation of the complete algorithm is developed and tests suggest that the diagnosis in an image can be estimated in shorter time than previous techniques with the same or better accuracy.
Keywords :
biomedical optical imaging; blood vessels; digital instrumentation; diseases; eye; feature extraction; medical image processing; neural nets; DRIVE database-evaluated image preprocessing method; MATLAB-implemented algorithm; automatic microaneurysm detection; correct diabetic retinopathy diagnosis; digital color fundus images; earliest diabetic retinopathy sign; fundus image captured; gray level 2D feature based vessel extraction; image enhancement; image morphological opening; image preprocessing step; image processing technique applications; image-based diagnostic estimation; microaneurysms; neural network based method; neural network-based retina image preprocessing; retinal image-based microaneurysm detection; retinal image-based microaneurysm identification; rule based methods; severe eye disease diagnosis; working age-associated blindness; Biomedical imaging; Blood vessels; Diabetes; Image color analysis; Image segmentation; Retina; Retinopathy; DR; exudate; feature extraction; microaneurysms; neovascularization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
Print_ISBN :
978-1-4799-6928-9
Type :
conf
DOI :
10.1109/CICN.2014.225
Filename :
7065645
Link To Document :
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