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Segmentation and Automatic Identification of Vasculature in Coronary Angiograms
Coronary angiography is the “gold standard” for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary ar...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516542/ https://www.ncbi.nlm.nih.gov/pubmed/34659446 http://dx.doi.org/10.1155/2021/2747274 |
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author | Liu, Yaofang Wan, Wenlong Zhang, Xinyue Liu, Shaoyu Liu, Yingdi Liu, Hu Zeng, Xueying Wang, Weiguo Zhang, Qing |
author_facet | Liu, Yaofang Wan, Wenlong Zhang, Xinyue Liu, Shaoyu Liu, Yingdi Liu, Hu Zeng, Xueying Wang, Weiguo Zhang, Qing |
author_sort | Liu, Yaofang |
collection | PubMed |
description | Coronary angiography is the “gold standard” for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method. |
format | Online Article Text |
id | pubmed-8516542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-85165422021-10-15 Segmentation and Automatic Identification of Vasculature in Coronary Angiograms Liu, Yaofang Wan, Wenlong Zhang, Xinyue Liu, Shaoyu Liu, Yingdi Liu, Hu Zeng, Xueying Wang, Weiguo Zhang, Qing Comput Math Methods Med Research Article Coronary angiography is the “gold standard” for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method. Hindawi 2021-10-07 /pmc/articles/PMC8516542/ /pubmed/34659446 http://dx.doi.org/10.1155/2021/2747274 Text en Copyright © 2021 Yaofang Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Yaofang Wan, Wenlong Zhang, Xinyue Liu, Shaoyu Liu, Yingdi Liu, Hu Zeng, Xueying Wang, Weiguo Zhang, Qing Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title | Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title_full | Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title_fullStr | Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title_full_unstemmed | Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title_short | Segmentation and Automatic Identification of Vasculature in Coronary Angiograms |
title_sort | segmentation and automatic identification of vasculature in coronary angiograms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516542/ https://www.ncbi.nlm.nih.gov/pubmed/34659446 http://dx.doi.org/10.1155/2021/2747274 |
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