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Extraction and reconstruction of retinal vasculature

Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and r...

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Main Authors: M.H.A., Fadzil, L.I., Izhar, P.A., Venkatachalam, T.V.N., Karunakar
Format: Citation Index Journal
Published: 2007
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utp.edu.my/317/1/paper.pdf
http://www.scopus.com/inward/record.url?eid=2-s2.0-36248987203&partnerID=40&md5=ab6c97ebb9d65a20e31efa8998b529b7
http://eprints.utp.edu.my/317/
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spelling my.utp.eprints.3172017-01-19T08:27:07Z Extraction and reconstruction of retinal vasculature M.H.A., Fadzil L.I., Izhar P.A., Venkatachalam T.V.N., Karunakar TK Electrical engineering. Electronics Nuclear engineering Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91-0.95, a sensitivity range (ability to detect vessel pixels) of 0.91-0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88-0.94. © 2007 Informa UK Ltd. 2007 Citation Index Journal PeerReviewed application/pdf http://eprints.utp.edu.my/317/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-36248987203&partnerID=40&md5=ab6c97ebb9d65a20e31efa8998b529b7 M.H.A., Fadzil and L.I., Izhar and P.A., Venkatachalam and T.V.N., Karunakar (2007) Extraction and reconstruction of retinal vasculature. [Citation Index Journal] http://eprints.utp.edu.my/317/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
M.H.A., Fadzil
L.I., Izhar
P.A., Venkatachalam
T.V.N., Karunakar
Extraction and reconstruction of retinal vasculature
description Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91-0.95, a sensitivity range (ability to detect vessel pixels) of 0.91-0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88-0.94. © 2007 Informa UK Ltd.
format Citation Index Journal
author M.H.A., Fadzil
L.I., Izhar
P.A., Venkatachalam
T.V.N., Karunakar
author_facet M.H.A., Fadzil
L.I., Izhar
P.A., Venkatachalam
T.V.N., Karunakar
author_sort M.H.A., Fadzil
title Extraction and reconstruction of retinal vasculature
title_short Extraction and reconstruction of retinal vasculature
title_full Extraction and reconstruction of retinal vasculature
title_fullStr Extraction and reconstruction of retinal vasculature
title_full_unstemmed Extraction and reconstruction of retinal vasculature
title_sort extraction and reconstruction of retinal vasculature
publishDate 2007
url http://eprints.utp.edu.my/317/1/paper.pdf
http://www.scopus.com/inward/record.url?eid=2-s2.0-36248987203&partnerID=40&md5=ab6c97ebb9d65a20e31efa8998b529b7
http://eprints.utp.edu.my/317/
_version_ 1738655052950667264
score 13.211869

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