Nonlinear filtering based on 3D wavelet transform for MRI denoising

Abstract

Magnetic resonance (MR) images are normally corrupted by random noise which makes the automatic feature extraction and analysis of clinical data complicated. Therefore, denoising methods have traditionally been applied to improve MR image quality. In this study, we proposed a 3D extension of the wavelet transform (WT)-based bilateral filtering for Rician noise removal. Due to delineating capability of wavelet, 3D WT was employed to provide effective representation of the noisy coefficients. Bilateral filtering of the approximation coefficients in a modified neighborhood improved the denoising efficiency and effectively preserved the relevant edge features. Meanwhile, the detailed subbands were processed with an enhanced NeighShrink thresholding algorithm. Validation was performed on both simulated and real clinical data. Using the peak signal-to-noise ratio (PSNR) to quantify the amount of noise of the MR images, we have achieved an average PSNR enhancement of 1.32 times with simulated data. The quantitative and the qualitative measures used as the quality metrics demonstrated the ability of the proposed method for noise cancellation.

Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI

“…Taking the characteristic distributions of MRI data into consideration, noise can be compensated. Numerous approaches have been proposed using MRI magnitude data to compensate for noise, using a variety of methods including total variation [23][24][25], analyzing multiple scales using wavelet denoising [26][27][28], via non-local means [13,22,[29][30][31] and linear minimum mean-square estimators (LMMSE) [14,32]. These approaches combine a mixture of techniques to handle the particular nature of MRI noise: spatialadaptation to the noise variance [11,24,29,33], Rician distribution [24,25,28,29,34] and accounting for Figure 1.…”

“…Exam was performed on a 1.5 T system using a Hologic endorectal receiver coil. signal-dependent bias when using a Gaussian assumption [11,[27][28][29].…”

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

“…These modifications may be lead to erroneous segmentation and feature extraction. Therefore, noise removing algorithms have been used to improve image quality [17][18][19][20]. In this study, the two-dimensional Bilateral filtering is used to denoise the MRI images.…”

“…The basic idea of Bilateral filtering is to weight the coefficients of filter with their associated relative pixel intensities to smooth images with edge-preserving. It is a non-iterative image smoothing scheme for edge-preserving by combined non-linearly nearby pixels [17]. The principle of Bilateral filtering is that every two pixels are close to each other if they occupy nearby spatial locations and have some similarity in the photometric range.…”

A new method for three-dimensional magnetic resonance images denoising

“…The use of wavelet transform for denoising is performed by applying a wavelet transform to noisy data as showing in Figure 1 (Wang et al, 2012), thresholding the resulting coefficients by comparing the detail coefficients with a given threshold value, and shrinking these coefficients close to zero to take away the effect of noise in the data (Donoho, 1995), and then applying an inverse transform of the thresholded wavelet coefficients to obtain a smoothed data. In terms of wavelet space decomposition, the separable 3D WT (Kroon et al, 2010) can be expressed by a tensor product by:…”