Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering
Abstract: A semiautomatic technique to detect the aortic root in three-dimensional multi-slice computerised tomography images is proposed. Three steps are considered: conditioning, filtering, and detection. The conditioning is based on multi-planar reconstruction and it is required for reformatting...
- Autores:
-
Valbuena, Oscar
Vera, Miguel Ángel
Del Mar, Atilio
Roa, Felida Andreina
Bravo, Antonio José
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/8378
- Palabra clave:
- Human heart
Aortic root
Multi-slice computerised tomography
MSCT
Segmentation
Similarity enhancement
Weighted median
Unsupervised clustering
- Rights
- embargoedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| title |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| spellingShingle |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering Human heart Aortic root Multi-slice computerised tomography MSCT Segmentation Similarity enhancement Weighted median Unsupervised clustering |
| title_short |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| title_full |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| title_fullStr |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| title_full_unstemmed |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| title_sort |
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering |
| dc.creator.fl_str_mv |
Valbuena, Oscar Vera, Miguel Ángel Del Mar, Atilio Roa, Felida Andreina Bravo, Antonio José |
| dc.contributor.author.none.fl_str_mv |
Valbuena, Oscar Vera, Miguel Ángel Del Mar, Atilio Roa, Felida Andreina Bravo, Antonio José |
| dc.subject.eng.fl_str_mv |
Human heart Aortic root Multi-slice computerised tomography MSCT Segmentation Similarity enhancement Weighted median Unsupervised clustering |
| topic |
Human heart Aortic root Multi-slice computerised tomography MSCT Segmentation Similarity enhancement Weighted median Unsupervised clustering |
| description |
Abstract: A semiautomatic technique to detect the aortic root in three-dimensional multi-slice computerised tomography images is proposed. Three steps are considered: conditioning, filtering, and detection. The conditioning is based on multi-planar reconstruction and it is required for reformatting the information to orthogonal planes to the aortic root. During the filtering, three nonlinear filters based on similarity enhancement, median and weighted median are considered to reduce noise and enhance the reformatted images. In the detection, the filtered volumes are processed with a clustering technique. Dice score, the point-to-mesh and the Hausdorff distances are used to compare the obtained results with respect to ground truth traced by a cardiologist. A clinical dataset of 90 volumes from 45 patients is used to validate the technique. The maximum Dice score (0.92), the minimum average point-to-mesh distance (0.96 mm) and the minimum average Hausdorff distance (4.80 mm) are obtained during preprocessed volumes segmentation using similarity enhancement. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-14T21:11:34Z |
| dc.date.available.none.fl_str_mv |
2021-09-14T21:11:34Z |
| dc.date.issued.none.fl_str_mv |
2021 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo científico |
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17526426 |
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https://hdl.handle.net/20.500.12442/8378 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1504/IJBET.2021.114811 |
| identifier_str_mv |
17526426 |
| url |
https://hdl.handle.net/20.500.12442/8378 https://doi.org/10.1504/IJBET.2021.114811 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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embargoedAccess |
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pdf |
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Inderscience Publishers |
| dc.source.eng.fl_str_mv |
International Journal of Biomedical Engineering and Technology (IJBET) |
| dc.source.none.fl_str_mv |
Vol. 35 N° 4 (2021) |
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Universidad Simón Bolívar |
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Valbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Vera, Miguel Ángelf883adfa-3a21-4326-9ba7-6c6b33f481c4Del Mar, Atilioa0cef2b7-97d7-4574-8c8d-ed84d44f7d17Roa, Felida Andreina9b667e73-e275-475b-b6c0-6a93df00c078Bravo, Antonio Joséfb9a908c-ea86-4f44-b5ff-615f5a6b4cab2021-09-14T21:11:34Z2021-09-14T21:11:34Z202117526426https://hdl.handle.net/20.500.12442/8378https://doi.org/10.1504/IJBET.2021.114811Abstract: A semiautomatic technique to detect the aortic root in three-dimensional multi-slice computerised tomography images is proposed. Three steps are considered: conditioning, filtering, and detection. The conditioning is based on multi-planar reconstruction and it is required for reformatting the information to orthogonal planes to the aortic root. During the filtering, three nonlinear filters based on similarity enhancement, median and weighted median are considered to reduce noise and enhance the reformatted images. In the detection, the filtered volumes are processed with a clustering technique. Dice score, the point-to-mesh and the Hausdorff distances are used to compare the obtained results with respect to ground truth traced by a cardiologist. A clinical dataset of 90 volumes from 45 patients is used to validate the technique. The maximum Dice score (0.92), the minimum average point-to-mesh distance (0.96 mm) and the minimum average Hausdorff distance (4.80 mm) are obtained during preprocessed volumes segmentation using similarity enhancement.pdfengInderscience PublishersAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfInternational Journal of Biomedical Engineering and Technology (IJBET)Vol. 35 N° 4 (2021)Human heartAortic rootMulti-slice computerised tomographyMSCTSegmentationSimilarity enhancementWeighted medianUnsupervised clusteringSemi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clusteringinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Aggarwal, S.R., Clavel, M.A., Messika-Zeitoun, D., Cueff, C., Malouf, J., Araoz, P.A., Mankad, R., Michelena, H., Vahanian, A. and Enriquez-Sarano, M. (2013) ‘Sex differences in aortic valve calcification measured by multidetector computed tomography in aortic stenosis’, Circulation: Cardiovascular Imaging, Vol. 6, No. 1, pp.40–47.Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L. and Erickson, B.J. (2017) ‘Deep learning for brain MRI segmentation: state of the art and future directions’, Journal of Digital Imaging, Vol. 30, No. 4, pp.449–459.Arce, G.R., Bacca, J. and Paredes, J.L. (2009) ‘Nonlinear filtering for image analysis and enhancement’, in Bovik, A. (Ed.): The Essential Guide to Image Processing, 2nd ed., pp.263–291, Academic Press, BostonAshok, V. and Murugesan, G. (2017) ‘Detection of retinal area from scanning laser ophthalmoscope images (SLO) using deep neural network’, Int. J. of Biomedical Engineering and Technology, Vol. 23, Nos. 2–4, pp.303–314.Ballard, D. (1981) ‘Generalizing the Hough transform to detect arbitrary shapes’, Pattern Recognition, Vol. 13, No. 2, pp.111–122.Barrett, J. and Keat, N. 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(2012) ‘Automatic aorta segmentation and valve landmark detection in C-arm CT for transcatheter aortic valve implantation’, IEEE Transaction on Medical Imaging, Vol. 31, No. 12, pp.2307–2321.CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/67e1ec56-b4b3-4e70-98c0-0f963188f201/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/4fd29ae3-7214-41e1-952d-e96a6da5aeb8/download733bec43a0bf5ade4d97db708e29b185MD5320.500.12442/8378oai:bonga.unisimon.edu.co:20.500.12442/83782024-08-14 21:52:23.633http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalmetadata.onlyhttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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 |