Mostrar el registro sencillo del ítem

dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.contributor.authorBarrera, D.
dc.contributor.authorValbuena, O.
dc.contributor.authorVera, M.
dc.contributor.authorHuérfano, Y.
dc.contributor.authorGelvez, E.
dc.contributor.authorSalazar, J.
dc.contributor.authorMolina, V.
dc.contributor.authorSáenz, F.
dc.contributor.authorVera, M I.
dc.contributor.authorSalazar, W.
dc.date.accessioned2019-03-06T21:08:02Z
dc.date.available2019-03-06T21:08:02Z
dc.date.issued2019
dc.identifier.issn09767673
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2739
dc.description.abstractThe 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.language.isoengeng
dc.publisherIOP Publishingeng
dc.sourceJournal of Physicseng
dc.sourceIOP Conf. Series: Journal of Physics: Conf. Series 1160 (2019) 012006eng
dc.source.uridoi:10.1088/1742-6596/1160/1/012006eng
dc.subjectComputerized emission tomographyeng
dc.subjectTransport theoryeng
dc.subjectCardiovascular systemeng
dc.titleIsotropic versus anisotropic techniques in cardiac computed tomography images processingeng
dc.typeConferenceeng
dcterms.referencesPratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)eng
dcterms.referencesGonzález R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall)eng
dcterms.referencesKelm 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 662-665eng
dcterms.referencesBuades A, Coll B and Morel J 2005 A review of image denoising algorithms with a new one Multiscale Modeling and Simulation 4(2) 490eng
dcterms.referencesBorsdorf 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) 1685eng
dcterms.referencesCoupé 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) 425eng
dcterms.referencesCollins 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) 463eng
dcterms.referencesRudin L, Osher S and Fatemi E 1992 Nonlinear total variation based noise removal algorithms Physica D 2 259eng
dcterms.referencesPerona P and Malik J 1990 Scalespace and edge detection using anisotropic diffusion IEEE Trans. Patt. Anal. Mach. Inte. 12(7) 629eng
dcterms.referencesGerig G, Kikinis R, Kbler O and Jolesz F 1992 Nonlinear anisotropic filtering of MRI data IEEE Trans. Med. Imag. 11(2) 221eng
dcterms.referencesKeeling S 2003 Total variation based on convex filters for medical imaging Applied Mathematics Computation 139 101eng
dcterms.referencesR. Nowak 1999 Wavelet–based rician noise removal for magnetic resonance imaging IEEE Trans. Med. Imag. 8(10) 1408eng
dcterms.referencesPassarielo G and Mora F. 1995 Imágenes médicas, adquisición, análisis, procesamiento e interpretación (Caracas: Equinoccio)spa
dcterms.referencesMeijering H 2000 Image enhancement in digital X ray angiography (Utrecht: Utrecht University)eng
dcterms.referencesKoenderink J 1984 The structure of images Biol. Cybern. 50 363eng
dcterms.referencesHans 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.referencesReal R and Vargas J 1996 The probabilistic basis of Jaccard's index of similarity Sys Biol 45(3) 380eng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos [1344]
    Artículos científicos evaluados por pares

Mostrar el registro sencillo del ítem