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. (2004) ‘Artifacts in CT: recognition and avoidance1’, Radiographics, Vol. 24, No. 6, pp.1679–1691Bravo, A. and Medina, R. (2008) ‘An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms’, Computerized Medical Imaging and Graphics, Vol. 32, No. 5, pp.396–408.Bravo, A., Clemente, J., Vera, M. and Medina, R. (2010) ‘A hybrid boundary-region left ventricle segmentation in computed tomography’, in VISSAPP, Angers, France. pp.107–114.Bravo, A., Vera, M., Garreau, M. and Medina, R. (2011) ‘Three-dimensional segmentation of ventricular heart chambers from multi-slice computerized tomography: an hybrid approach’, in DICTAP, Springer, Vol. 166 of CCIS, pp.287–301.Canny, J. (1986) ‘A computational approach to edge detection’, IEEE Transaction on Pattern Recognition Analysis and Machine Inteligence PAMI, Vol. 8, No. 6, pp.679–698.Cribier, A., Eltchaninoff, H., Bash, A., Borenstein, N., Tron, C., Bauer, F., Derumeaux, G., Anselme, F., Laborde, F. and Leon, M.B. (2002) ‘Percutaneous transcatheter implantation of an aortic valve prosthesis for calcific aortic stenosis: first human case description’, Circulation, Vol. 106, No. 24, pp.3006–3008.Dice, L. (1945) ‘Measures of the amount of ecologic association between species’, Ecology, Vol. 26, No. 3, pp.297–302.Elattar, M., Wiegerinck, E., Planken, R., Vanbavel, E., Van Assen, H., Baan, J. and Marquering, H. (2014) ‘Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation’, Medical & Biological Engineering & Computing, Vol. 52, No. 7, pp.611–618.Faletra, F., Pandian, N. and Ho, S. (2008) Anatomy of the Heart by Multislice Computed Tomography, Wiley-Blackwell, West Sussex, UK.Fauci, A.S. (2008) Harrison’s Principles of Internal Medicine, McGraw-Hill, New York.Fernandez-Perez, N., Gonzalez-Lopez, S., Rodriguez-Rivero, C., Ciobanu, C. and Saint-Pierre, G. (2012) ‘FE analysis applied for validation of a biostable aortic valve replacement device: stent and leaflet material selection’, Int. J. of Biomedical Engineering and Technology, Vol. 9, No. 4, pp.378–394.Feuchtner, G.M., Stolzmann, P., Dichtl, W., Schertler, T., Bonatti, J., Scheffel, H., Mueller, S., Plass, A., Mueller, L., Bartel, T., Wolf, F. and Alkadhi, H. (2009) ‘Multislice computed tomography in infective endocarditis: comparison with transesophageal echocardiography and intraoperative findings’, Journal of the American College of Cardiology, Vol. 53, pp.436–444.Flohr, T.G., Schaller, S., Stierstorfer, K., Bruder, H., Ohnesorge, B.M. and Schoepf, U.J. (2005) Multi-detector row CT systems and image – reconstruction techniques’, Radiology, Vol. 235, No. 5, pp.756–773.Ghesu, F.C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J. and Comaniciu, D., (2016) ‘Marginal space deep learning: Efficient architecture for volumetric image parsing’, IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp.1217–1228.Ginat, D.T. and Gupta, R. (2014) ‘Advances in computed tomography imaging technology’, Annual Review of Biomedical Engineering, Vol. 16, No. 1, pp.431–453.Gonzalez, R. and Woods, R. (2006) Digital Image Processing, 3rd ed., Prentice-Hall,New Jersey, USA.Grbic, S., Ionasec, R., Vitanovski, D., Voigt, I., Wang, Y., Georgescu, B., Navab, N. and Comaniciu, D. (2012) ‘Complete valvular heart apparatus model from 4D cardiac CT’, Medical Image Analysis, Vol. 16, No. 5, pp.1003–1014.Grbic, S., Mansi, T., Ionasec, R., Voigt, I., Houle, H., John, M., Schoebinger, M., Navab, N. and Comaniciu, D. (2013) ‘Image-based computational models for TAVI planning: from CT images to implant deployment’, in Mori, K., Sakuma, I., Sato, Y., Barillot, C. and Navab, N. (Eds.): Medical Image Computing and Computer-Assisted Intervention, pp.395–402, Springer Berlin Heidelberg.Grube, E., Laborde, J.C., Zickmann, B., Gerckens, U., Felderhoff, T., Sauren, B., Bootsveld, A., Buellesfeld, L. and Iversen, S. (2005) ‘First report on a human percutaneous transluminal implantation of a self-expanding valve prosthesis for interventional treatment of aortic valve stenosis’, Catheterization and Cardiovascular Interventions, Vol. 66, No. 4, pp.465–469.Gupta, S., Chakarvarti, S. and Zaheeruddin (2016) ‘Medical image registration based on fuzzy c-means clustering segmentation approach using SURF’, Int. J. of Biomedical Engineering and Technology, Vol. 20, No. 1, pp.33–50.Guyton, A. and Hall, J. (2006) Textbook of Medical Physiology, Elsevier Saunders, USA.Haralick, R. and Shapiro, L. (1992) Computer and Robot Vision, Vol. 1, Addison-Wesley, USA.Ho, S.Y. (2002) ‘Anatomy of the mitral valve’, Heart, Vol. 88, No. 4, pp.iv5–iv10.Ho, S.Y. (2009) ‘Structure and anatomy of the aortic root’, European Heart Journal – Cardiovascular Imaging, Vol. 10, No. 1, pp.i3–i10.Huttenlocher, D., Klanderman, G. and Rucklidge, W. (1993) ‘Comparing images using the Hausdorff distance’, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 9, pp.850–863.Ibáñez, L., Schroeder, W., Ng, L. and Cates, J. (2003) The ITK Software Guide, Kitware, USA.Ji, Z.X., Sun, Q.S. and Xia, D.S. (2011) ‘A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image’, Computerized Medical Imaging and Graphics, Vol. 35, No. 5, pp.383–397.Kirisli, H., Schaap, M., Klein, S., Papadopoulou, S., Bonardi, M., Chen, C., Weustink, A., Mollet, N., Vonken, E.P.A., Van der Geest, R., Van Walsum, T. and Niessen, W. (2010) ‘Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multi-center and multi-vendor study’, Medical Physics, Vol. 37, No. 12, pp.6279–6292.Lorenz, C. and Von Berg, J. (2006) ‘A comprehensive shape model of the heart’, Medical Image Analysis, Vol. 10, No. 4, pp.657–670.Manghat, N., Rachapalli, V., Van Lingen, R., Veitch, A., Roobottom, C. and Morgan-Hughes, G. (2008) ‘Imaging the heart valves using ECG-gated 64-detector row cardiac CT’, The British Journal of Radiology, Vol. 81, No. 964, pp.275–290.MPPS (2012) Anuario deMortalidad, Technical Report.Ministerio del Poder Popular para la Salud, República Bolivariana de Venezuela, Caracas.Pauwels, E. and Frederix, G. (1999) ‘Finding salient regions in images: non-parametric clustering for image segmentation and grouping’, Computer Vision and Image Understanding, Vol. 18, Nos. 1–2, pp.73–85.Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E. and Nielsen, M. (2013) ‘Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network’, in Mori, K., Sakuma, I., Sato, Y., Barillot, C. and Navab, N. (Eds.): Medical Image Computing and Computer-Assisted Intervention, Springer Berlin Heidelberg. pp.246–253.Pratt, W. (2007) Digital Image Processing, John Wiley Sons, Los Altos.Primak, A., McCollough, C., Bruesewitz, M., Zhang, J. and Fletcher, J. (2006) ‘Relationship between noise, dose, and pitch in cardiac multi-detector row CT’, Radiographics, Vol. 26, No. 6, pp.1785–1794.Rogers, J.H. and Bolling, S.F. (2009) ‘The tricuspid valve: current perspective and evolving management of tricuspid regurgitation’, Circulation, Vol. 119, No. 20, pp.2718–2725.Rubin, G.D. (2014) ‘Computed tomography: revolutionizing the practice of medicine for 40 years’, Radiology, Vol. 273, Suppl. 2, pp.S45–S74.Ryan, R., Abbara, S., Colen, R.R., Arnous, S., Quinn, M., Cury, R.C. and Dodd1, J.D. (2008) ‘Cardiac valve disease: spectrum of findings on cardiac 64-MDCT’, American Journal of Roentgenology, Vol. 190, No. 5, pp.W294–W303.Sharma, N. and Aggarwal, L. (2010) ‘Automated medical image segmentation techniques’, Journal of Medical Physics, Vol. 35, No. 1, pp.3–14.Sharma, N., Ray, A., Sharma, S., Shukla, K., Pradhan, S. and Aggarwal, L. (2008) ‘Segmentation and classification of medical images using texture-primitive features: application of BAM-type artificial neural network’, Journal of Medical Physics, Vol. 33, No. 3, pp.119–126.Stamm, C., Anderson, R.H. and Ho, S.Y. (1998) ‘Clinical anatomy of the normal pulmonary root compared with that in isolated pulmonary valvular stenosis 1’, Journal of the American College of Cardiology, Vol. 31, No. 6, pp.1420–1425.Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B. and Liang, J. (2016) ‘Convolutional neural networks for medical image analysis: full training or fine tuning?’, IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp.1299–1312.Watanabe, Y., Chevalier, B., Hayashida, K., Leong, T., Bouvier, E., Arai, T., Farge, A., Hovasse, T., Garot, P., Cormier, B., Morice, M.C. and Lefèvre, T. (2015) ‘Comparison of multislice computed tomography findings between bicuspid and tricuspid aortic valves before and after transcatheter aortic valve implantation’, Catheterization and Cardiovascular Interventions, Vol. 86, No. 2, pp.323–330.WHO (2011) Global Status Report on Non Communicable Diseases, The World Health Report 2010 Geneva, World Health Organization.Yin, L., Yang, R., Gabbouj, M. and Neuvo, Y. (1996) ‘Weighted median filters: a tutorial’, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, Vol. 43, No. 3, pp.157–192.Zheng, Y., John, M., Liao, R., Nöttling, A., Boese, J.M., Kempfert, J., Walther, T., Brockmann, G. and Comaniciu, D. (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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 |