dc.creatorContreras, Yudith
dc.creatorVera, Miguel
dc.creatorHuérfano, Yoleidy
dc.creatorValbuena, Oscar
dc.creatorSalazar, Williams
dc.creatorVera, María Isabel
dc.creatorBorrero, Maryury
dc.creatorBarrera, Doris
dc.creatorHernández, Carlos
dc.creatorMolina, Ángel Valentín
dc.creatorMartínez, Luis Javier
dc.creatorSáenz, Frank
dc.creatorVivas, Marisela
dc.creatorSalazar, Juan
dc.creatorGelvez, Elkin
dc.date.accessioned2019-01-25T16:23:23Z
dc.date.accessioned2022-11-14T19:50:30Z
dc.date.available2019-01-25T16:23:23Z
dc.date.available2022-11-14T19:50:30Z
dc.date.created2019-01-25T16:23:23Z
dc.date.issued2018
dc.identifier18564550
dc.identifierhttp://hdl.handle.net/20.500.12442/2527
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5183413
dc.description.abstractIn this work an adaptation of the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, in the context of digital medical image processing is proposed. Specifically, synthetic images reported in the literature are used as numerical phantoms. Construction of the synthetic images was inspired by a detailed analysis of some of the imperfections found in the real multilayer cardiac computed tomography images. Of all the imperfections considered, only Poisson noise was selected and incorporated into a synthetic database. An example is presented in which images contaminated with Poisson noise are processed and then subject to two classical digital smoothing techniques, identified as Gaussian filter and anisotropic diffusion filter. Additionally, the peak of the signal-to-noise ratio (PSNR) is considered as a metric to analyze the performance of these filters.
dc.languageeng
dc.publisherSociedad Latinoamericana de Hipertensión
dc.rightsLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceRevista Latinoamericana de Hipertensión
dc.sourceVol. 13, No. 4 (2018)
dc.sourcehttp://www.revhipertension.com/rlh_4_2018/1_digital_processing_of_medical.pdf
dc.subjectCRISP-DM Methodology
dc.subjectSynthetic cardiac images
dc.subjectComputerized tomography
dc.subjectNoise
dc.subjectArtifacts
dc.titleDigital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology
dc.typearticle


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