Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 InternacionalContreras, YudithVera, MiguelHuérfano, YoleidyValbuena, OscarSalazar, WilliamsVera, María IsabelBorrero, MaryuryBarrera, DorisHernández, CarlosMolina, Ángel ValentínMartínez, Luis JavierSáenz, FrankVivas, MariselaSalazar, JuanGelvez, Elkin2019-01-252019-01-25201818564550http://hdl.handle.net/20.500.12442/2527In 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.engCRISP-DM MethodologySynthetic cardiac imagesComputerized tomographyNoiseArtifactsarticleinfo:eu-repo/semantics/openAccess