Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology
Cargando...
Fecha
2018
Autores
Contreras, Yudith
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Barrera, Doris
Hernández, Carlos
Molina, Ángel Valentín
Título de la revista
ISSN de la revista
Título del volumen
Editor
Sociedad Latinoamericana de Hipertensión
Resumen
In 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.
Descripción
Palabras clave
CRISP-DM Methodology, Synthetic cardiac images, Computerized tomography, Noise, Artifacts