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Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO)
We present a methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal vide...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Optical Society of America
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3269849/ https://www.ncbi.nlm.nih.gov/pubmed/22312585 http://dx.doi.org/10.1364/BOE.3.000327 |
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author | Estrada, Rolando Tomasi, Carlo Cabrera, Michelle T. Wallace, David K. Freedman, Sharon F. Farsiu, Sina |
author_facet | Estrada, Rolando Tomasi, Carlo Cabrera, Michelle T. Wallace, David K. Freedman, Sharon F. Farsiu, Sina |
author_sort | Estrada, Rolando |
collection | PubMed |
description | We present a methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online. |
format | Online Article Text |
id | pubmed-3269849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Optical Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-32698492012-02-06 Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) Estrada, Rolando Tomasi, Carlo Cabrera, Michelle T. Wallace, David K. Freedman, Sharon F. Farsiu, Sina Biomed Opt Express Image Processing We present a methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online. Optical Society of America 2012-01-18 /pmc/articles/PMC3269849/ /pubmed/22312585 http://dx.doi.org/10.1364/BOE.3.000327 Text en © 2012 Optical Society of America http://creativecommons.org/licenses/by-nc-nd/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially. |
spellingShingle | Image Processing Estrada, Rolando Tomasi, Carlo Cabrera, Michelle T. Wallace, David K. Freedman, Sharon F. Farsiu, Sina Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title | Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title_full | Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title_fullStr | Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title_full_unstemmed | Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title_short | Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO) |
title_sort | exploratory dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (vio) |
topic | Image Processing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3269849/ https://www.ncbi.nlm.nih.gov/pubmed/22312585 http://dx.doi.org/10.1364/BOE.3.000327 |
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