Comparación de Algoritmos de Segmentación de Ruido Aplicados a Imágenes de Resonancia Magnética

in the study and diagnosis of pediatric patients with tumors in the central nervous system (cns) magnetic resonance imaging (mri) is used to quantify, evaluate and document therapies and treatments. these images are affected by noise, mainly from the movement of the patient in the study period and in the process of image acquisition. in this paper we propose two simple and efficient algorithms for noise segmentation in images, analyzing its histogram feature. these noise segmentation techniques produce images which have been segmented and reset a number of pixels, making it possible to reduce the computation time in subsequent processing. also, comparisons between the algorithms are made by measuring the computation time, the mean relative error, the number of points that are set to zero and other variables of interest.