Wavelet Based Denoising of MRI Images

Magnetic resonance imaging (MRI) is used primarily in medical fields to produce images of the internal structure of human body. It is a potentially useful and effective diagnostic tool in basic research, clinical investigation, and disease diagnosis since it provides both chemical and physiological information about the tissue under investigation. Medical images like MR images are textured images and contain ridges and edges. It is well known that the noise in MR images obey a Rician distribution. Rician noise is not zero-mean, and the mean depends on the local intensity in the image. Because of this complication, magnetic resonance image estimation from noisy data is especially challenging. Many algorithms have been developed for MRI denoising as reported in the literature, but MRI denoising still remains a challenge for researchers because most of these consider the incorporated noise as additive Gaussian white, removal of which introduces artifacts and causes blurring of the images. In the recent years there has been a fair amount of research on filtering and wavelet coefficients thresholding, because wavelets provide an appropriate basis for separating noisy signal from the image signal. Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images. This paper proposes wavelet domain denoising for the removal of Rician noise from MR images. The proposed method depends heavily on the choice of threshold parameter, which in turn determines the efficacy of denoising. Experiments show that the proposed scheme better suppresses noise and preserves edges. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless. The quality of the enhanced images is measured by the statistical quantity measures: Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE).