Statistical techniques for digital pre-processing of computed tomography medical images: A current review
Oscar Valbuena, Miguel Ángel Vera, Guillermo Ramirez, Ricardo Barrientos Rojel, David Mojica Maldonado · Displays · 2024
Digital pre-processing is a vital stage in the processing of the information contained in multilayer computed tomography images. The purpose of digital pre-processing is the minimization of the effect of image imperfections, which are associated with the noise and artifacts that affect the quality of the images during acquisition, storage, and/or transmission processes. Likewise, there is a wide variety of techniques in specialized literature that address the problem of imperfections, noise, and artifacts present in images. In this study, a comprehensive review of specialized literature on statistical techniques used in the pre-processing of digital images was conducted. The review summarizes updated information from 56 studies conducted over the last 5 years (2018–2022) on the main statistical techniques used for the digital processing of medical images obtained under different modalities, with a special focus on computed tomography . Additionally, the most often used statistical metrics for measuring the performance of pre-processing techniques in the field of medical imaging are described. The most often used pre-processing techniques in the field of medical imaging were found to be statistical filters based on median, neural networks , Gaussian filters based on deep learning , mean, and machine learning applied to multilayer computed tomography images and magnetic resonance images of the brain, abdomen, lungs, and heart, among other organs of the body.