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A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation

The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment...

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Autores principales: Khawaja, Ahsan, Khan, Tariq M., Khan, Mohammad A. U., Nawaz, Syed Junaid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891360/
https://www.ncbi.nlm.nih.gov/pubmed/31766276
http://dx.doi.org/10.3390/s19224949
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author Khawaja, Ahsan
Khan, Tariq M.
Khan, Mohammad A. U.
Nawaz, Syed Junaid
author_facet Khawaja, Ahsan
Khan, Tariq M.
Khan, Mohammad A. U.
Nawaz, Syed Junaid
author_sort Khawaja, Ahsan
collection PubMed
description The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.
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spelling pubmed-68913602019-12-12 A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid Sensors (Basel) Article The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation. MDPI 2019-11-13 /pmc/articles/PMC6891360/ /pubmed/31766276 http://dx.doi.org/10.3390/s19224949 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khawaja, Ahsan
Khan, Tariq M.
Khan, Mohammad A. U.
Nawaz, Syed Junaid
A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title_full A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title_fullStr A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title_full_unstemmed A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title_short A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
title_sort multi-scale directional line detector for retinal vessel segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891360/
https://www.ncbi.nlm.nih.gov/pubmed/31766276
http://dx.doi.org/10.3390/s19224949
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