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Reseach Article

Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique

Published on December 2013 by C. Aravind, M. Ponnibala, S. Vijayachitra
International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
Foundation of Computer Science USA
ICIIIOES - Number 11
December 2013
Authors: C. Aravind, M. Ponnibala, S. Vijayachitra
5a6fada0-bdd1-49a4-903b-0c29ad418002

C. Aravind, M. Ponnibala, S. Vijayachitra . Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique. International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences. ICIIIOES, 11 (December 2013), 18-22.

@article{
author = { C. Aravind, M. Ponnibala, S. Vijayachitra },
title = { Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique },
journal = { International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences },
issue_date = { December 2013 },
volume = { ICIIIOES },
number = { 11 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 18-22 },
numpages = 5,
url = { /proceedings/iciiioes/number11/14360-1376/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%A C. Aravind
%A M. Ponnibala
%A S. Vijayachitra
%T Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique
%J International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%@ 0975-8887
%V ICIIIOES
%N 11
%P 18-22
%D 2013
%I International Journal of Computer Applications
Abstract

At present, Diabetic Retinopathy was considered as the main cause of blindness for diabetic patients. The Diabetic Retinopathy can be identified at an earlier stage by detecting the microaneurysms in the retina of the patients. For this purpose, opthalmologists will regularly supervise the retinal images obtained using the color fundus camera. During this regular supervision the ophthalmologists should spend more amount of time and energy. The space required to store the normal and abnormal retinal images will also increases. A new method for detecting the microaneurysms from the color fundus retinal image based on feature classification was proposed in this project, to reduce the ophthalmologists' time and energy for verifying the retinal images. The microaneurysms are detected from the color fundus image by applying the preprocessing techniques inorder to remove the optic disk and similar blood vessels using morphological operations. The preprocessed image was then used for feature extraction and these features were used for classification purpose. The classifier used is Support Vector Machine which improves sensitivity, specificity and gives an average accuracy of 90%.

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Index Terms

Computer Science
Information Sciences

Keywords

Diabetic Retinopathy (dr) Microaneurysms Morphology Support Vector Machine.