Abstract:
With advanced imaging techniques, Magnetic
Resonance Imaging (MRI) plays an important role in
medical environments to create high quality images
contained in the human organs. In the processing of
medical images, medical images are coordinated by
different types of noise. It is very important to
acquire accurate images and observe specific
applications with precision. Currently, eliminating
noise from medical images is a very difficult problem
in the field of medical image processing. In this
document, three types of noise, Gaussian noise, and
salt & pepper noise, uniform noise are tested and the
tested variances of Gaussian noise and uniform noise
are 0.02 and 10 respectively. We analyze the kernel
value or the window size of the medium filter and the
Wiener filter. All experimental results are tested on
MRI images of the BRATS data set, the DICOM data
set and TCIA data set. MRI brain images are
obtained from the BRATS data set and the DICOM
data set, the MRI bone images are obtained from the
TCIA data set. The quality of the output image is
measured by statistical measurements, such as the
peak signal noise ratio (PSNR) and the root mean
square error (RMSE).
