Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images
We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477058/ https://www.ncbi.nlm.nih.gov/pubmed/22695250 http://dx.doi.org/10.1186/1476-511X-11-73 |
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author | Pachiyappan, Arulmozhivarman Das, Undurti N Murthy, Tatavarti VSP Tatavarti, Rao |
author_facet | Pachiyappan, Arulmozhivarman Das, Undurti N Murthy, Tatavarti VSP Tatavarti, Rao |
author_sort | Pachiyappan, Arulmozhivarman |
collection | PubMed |
description | We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized. |
format | Online Article Text |
id | pubmed-3477058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34770582012-10-23 Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images Pachiyappan, Arulmozhivarman Das, Undurti N Murthy, Tatavarti VSP Tatavarti, Rao Lipids Health Dis Research We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized. BioMed Central 2012-06-13 /pmc/articles/PMC3477058/ /pubmed/22695250 http://dx.doi.org/10.1186/1476-511X-11-73 Text en Copyright ©2012 Pachiyappan et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Pachiyappan, Arulmozhivarman Das, Undurti N Murthy, Tatavarti VSP Tatavarti, Rao Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title | Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title_full | Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title_fullStr | Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title_full_unstemmed | Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title_short | Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images |
title_sort | automated diagnosis of diabetic retinopathy and glaucoma using fundus and oct images |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477058/ https://www.ncbi.nlm.nih.gov/pubmed/22695250 http://dx.doi.org/10.1186/1476-511X-11-73 |
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