| dc.description.abstract | Magnetic Resonance Imaging is one of the most advanced and
effective medical diagnosis methods ,however the raw image
data is normally corrupted by random noise from the
measurement process this reduces the accuracy and reliability
of the results. Denoising methods are often used to increase
the Signal-to-Noise Ratio (SNR) and improve image clarity
.In this paper an adaptive Non-Local Means filter is developed
in which bilateral filter is used to pre-enhance the images and
then multi resolution wavelet domain is used to remove
coefficients that contain more noise than signal.
In the past different methods have been used to denoise MRI
images but many have not taken into consideration the Rician
nature of noise distribution therefore they have not been very
effective .Adaptation in this case is based on frequency and
spatial information obtained from the noisy image.
Knowledge of level of noise is used in an optimization
procedure to minimize a Rician based likelihood function and
by use of square signal intensity bias is also discarded. The
method is implemented in Matlab and MRI images with
different level of artificial noise are denoised using the
algorithm. Measures of performance values are PSNR,
37.12dB, MSE, 15.23, UQI, 0.985, SSIM, 0.894 , EPI,0.69 for
a 10% noisy image. These and also visual inspection show
that there is significant improvement from results obtained
using stand alone methods such as Gaussian smoothing,
Wiener filter, NLM filter ,bilateral filter and wavelet
thresholding.
General Terms
Medical image processing, denoising, measures of quality,
magnetization vector, adaptive multi resolution, total
variation, wavelet coefficient thresholding ,Non-Local Means,
frequency localization, proton spin density, discrete inverse
Fourier transform, phase unwrapping, signal-dependent bias,
local neighbourhood, Rician-based log-likelihood function. | en_US |
