Kernels Analysis in MRI Images Noise Removal Methods

Abstract

With advanced imaging techniques, MagneticResonance Imaging (MRI) plays an important role inmedical environments to create high quality imagescontained in the human organs. In the processing ofmedical images, medical images are coordinated bydifferent types of noise. It is very important toacquire accurate images and observe specificapplications with precision. Currently, eliminatingnoise from medical images is a very difficult problemin the field of medical image processing. In thisdocument, three types of noise, Gaussian noise, andsalt & pepper noise, uniform noise are tested and thetested variances of Gaussian noise and uniform noiseare 0.02 and 10 respectively. We analyze the kernelvalue or the window size of the medium filter and theWiener filter. All experimental results are tested onMRI images of the BRATS data set, the DICOM dataset and TCIA data set. MRI brain images areobtained from the BRATS data set and the DICOMdata set, the MRI bone images are obtained from theTCIA data set. The quality of the output image ismeasured by statistical measurements, such as thepeak signal noise ratio (PSNR) and the root meansquare error (RMSE)