Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision los...

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Main Authors: D. Siva Sundhara Raja, S. Vasuki
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2015/419279
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spelling doaj-d906de6558aa4bcf8e2a4e563d16e8a42020-11-24T22:10:29ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182015-01-01201510.1155/2015/419279419279Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy DiagnosisD. Siva Sundhara Raja0S. Vasuki1Department of ECE, SACS MAVMM Engineering College, Madurai, Tamil Nadu 625 301, IndiaDepartment of ECE, Velammal College of Engineering and Technology, Madurai, Tamil Nadu 625 009, IndiaDiabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.http://dx.doi.org/10.1155/2015/419279
collection DOAJ
language English
format Article
sources DOAJ
author D. Siva Sundhara Raja
S. Vasuki
spellingShingle D. Siva Sundhara Raja
S. Vasuki
Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
Computational and Mathematical Methods in Medicine
author_facet D. Siva Sundhara Raja
S. Vasuki
author_sort D. Siva Sundhara Raja
title Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
title_short Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
title_full Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
title_fullStr Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
title_full_unstemmed Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis
title_sort automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2015-01-01
description Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.
url http://dx.doi.org/10.1155/2015/419279
work_keys_str_mv AT dsivasundhararaja automaticdetectionofbloodvesselsinretinalimagesfordiabeticretinopathydiagnosis
AT svasuki automaticdetectionofbloodvesselsinretinalimagesfordiabeticretinopathydiagnosis
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