Isotropic versus anisotropic techniques in cardiac computed tomography images processing
dc.contributor.author | Barrera, D. | |
dc.contributor.author | Valbuena, O. | |
dc.contributor.author | Vera, M. | |
dc.contributor.author | Huérfano, Y. | |
dc.contributor.author | Gelvez, E. | |
dc.contributor.author | Salazar, J. | |
dc.contributor.author | Molina, V. | |
dc.contributor.author | Sáenz, F. | |
dc.contributor.author | Vera, M I. | |
dc.contributor.author | Salazar, W. | |
dc.date.accessioned | 2019-03-06T21:08:02Z | |
dc.date.available | 2019-03-06T21:08:02Z | |
dc.date.issued | 2019 | |
dc.description.abstract | The objective of the work is to compare the performance of two filters, one isotropic and another one of anisotropic diffusion based on gradient. To do this, experiments are carried out to establish which of the filters exhibits a better behavior against the imperfections that characterize the computed tomography images. The structure of the experiments is as follows: a) The parameters linked to the aforementioned filters are identified. b) The ranges of valuesof these parameters and the way to use them are established. c) A database of three-dimensional cardiac images is filtered by applying, independently, the aforementioned filters considering a pre-established subset of values associated with the parameters. d) All the filtered images are addressed by a segmentation process, based on the growth of regions, which allows extracting the 3D morphology of the thoracic external aorta. e) As a metric to evaluate the performance of each technique, the Jaccard similarity index (JSI) is used. f) The one that generates the lowest calculated JSI is selected as the best technique when comparing a reference segmentation with all generated segmentations. The results indicate that the anisotropic diffusion filter, based on a gradient, obtained the best performance. | eng |
dc.identifier.issn | 09767673 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12442/2739 | |
dc.language.iso | eng | eng |
dc.publisher | IOP Publishing | eng |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
dc.source | Journal of Physics | eng |
dc.source | IOP Conf. Series: Journal of Physics: Conf. Series 1160 (2019) 012006 | eng |
dc.source.uri | doi:10.1088/1742-6596/1160/1/012006 | eng |
dc.subject | Computerized emission tomography | eng |
dc.subject | Transport theory | eng |
dc.subject | Cardiovascular system | eng |
dc.title | Isotropic versus anisotropic techniques in cardiac computed tomography images processing | eng |
dc.type | Conference | eng |
dcterms.references | Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc) | eng |
dcterms.references | González R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall) | eng |
dcterms.references | Kelm Z, Blezek D, Bartholmai B and Erickson B 2009 Optimizing non-local means for denoising low dose CT IEEE International Symposium on Biomedical Imaging: From Nano to Macro (Boston: IEEE Press) p 662-665 | eng |
dcterms.references | Buades A, Coll B and Morel J 2005 A review of image denoising algorithms with a new one Multiscale Modeling and Simulation 4(2) 490 | eng |
dcterms.references | Borsdorf A, Raupach R, Flohr T and Hornegger J 2008 Wavelet based noise reduction in ct–images using correlation analysis IEEE Trans. Med. Imag. 27(12) 1685 | eng |
dcterms.references | Coupé P, Yger P, Prima S, Hellier P, Kervrann C and Barillot C 2008 An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images IEEE Trans. Med. Imag. 27(4) 425 | eng |
dcterms.references | Collins D, Zijdenbos A, Kollokian V, Sled J, Kabani N, Holmes C and Evans A 1998 Design and construction of a realistic digital brain phantom IEEE Trans. Med. Imag. 17(3) 463 | eng |
dcterms.references | Rudin L, Osher S and Fatemi E 1992 Nonlinear total variation based noise removal algorithms Physica D 2 259 | eng |
dcterms.references | Perona P and Malik J 1990 Scalespace and edge detection using anisotropic diffusion IEEE Trans. Patt. Anal. Mach. Inte. 12(7) 629 | eng |
dcterms.references | Gerig G, Kikinis R, Kbler O and Jolesz F 1992 Nonlinear anisotropic filtering of MRI data IEEE Trans. Med. Imag. 11(2) 221 | eng |
dcterms.references | Keeling S 2003 Total variation based on convex filters for medical imaging Applied Mathematics Computation 139 101 | eng |
dcterms.references | R. Nowak 1999 Wavelet–based rician noise removal for magnetic resonance imaging IEEE Trans. Med. Imag. 8(10) 1408 | eng |
dcterms.references | Passarielo G and Mora F. 1995 Imágenes médicas, adquisición, análisis, procesamiento e interpretación (Caracas: Equinoccio) | spa |
dcterms.references | Meijering H 2000 Image enhancement in digital X ray angiography (Utrecht: Utrecht University) | eng |
dcterms.references | Koenderink J 1984 The structure of images Biol. Cybern. 50 363 | eng |
dcterms.references | Hans J. Johnson, Matthew M. McCormick, Luis Ibáñez and the Insight Software Consortium 2018 The ITK Software Guide Fourth Edition (New York: Kitware Inc.) | eng |
dcterms.references | Real R and Vargas J 1996 The probabilistic basis of Jaccard's index of similarity Sys Biol 45(3) 380 | eng |