A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images

Rajan, Jeny; Dekker, Arnold J. den; Juntu, Jaber; Sijbers, Jan

doi:10.1007/978-3-642-45062-4_62

Cited by 4 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its clinical application is more and more extensive. But by other factors, the original MRI images may appear value of impulse noise, artifacts, and image blurring, so the need for MRI image enhancement processing, highlighting the important features of medicine, improve the quality of image, and make accurate judgment on the condition, which is convenient for the doctor 33,37,38 …”

Section: Mri Image Denoisingmentioning

confidence: 99%

“…But by other factors, the original MRI images may appear value of impulse noise, artifacts, and image blurring, so the need for MRI image enhancement processing, highlighting the important features of medicine, improve the quality of image, and make accurate judgment on the condition, which is convenient for the doctor. 33,38,39 Medical image denoising is the display of medical images of the human body in some tissues or organs of the shape, boundary, cross-sectional area, and volume of accurate measurement to get important information on the pathology or function of the essential technology; it is also the premise of computer aided therapy, 3D reconstruction, and visualization of medical images. Therefore, the research of medical image segmentation has been paid more and more attention for many years.…”

Section: Mri Image Denoisingmentioning

confidence: 99%

See 1 more Smart Citation

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Wang

Huang

Zhang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Image denoising based on convolutional neural networks and wavelet transform is a novel approach for the applications. In the image acquisition process, images are often contaminated by noise, which affects the image quality; therefore, it is necessary to eliminate noise before analyzing and using images. Wavelet analysis is a local analysis method with multi-resolution characteristics, which is developed on the basis of short-time Fourier transform. It can be used for the multi-scale analysis of signals by means of expansion, translation and other operations, and extracting effective information from signals, which is a powerful tool for analyzing non-stationary signals. Wavelet has good time-frequency local characteristics, low entropy, and decorrelation. In this paper, we propose MRI image denoising framework based on convolutional neural networks and wavelet transform, and the experiment results show that the proposed method can keep the edge and curvature structure better while denoising. Compared with the other novel methodologies, the proposed algorithm can provide the higher robustness. In the future research, we will try the implementations of the methodologies.

show abstract

Section: Mri Image Denoisingmentioning

confidence: 99%

Section: Mri Image Denoisingmentioning

confidence: 99%

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Wang

Huang

Zhang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…1, but with σ taken as known a priori . As expected, this prior knowledge results in more rapid convergence and more accurate estimation of A k [17]–[18]. The intensities S k ( m ) are obtained from the subset of M voxels similar to i as described in section II.1.2.…”

Section: Theorymentioning

confidence: 92%

Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter

Bouhrara

Bonny²,

Ashinsky

et al. 2017

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Denoising of magnetic resonance (MR) images enhances diagnostic accuracy, the quality of image manipulations such as registration and segmentation, and parameter estimation. The first objective of this paper is to introduce a new, high-performance, nonlocal filter for noise reduction in MR image sets consisting of progressively-weighted, that is, multispectral, images. This filter is a multispectral extension of the nonlocal maximum likelihood filter (NLML) combining both spatial and spectral information, that is, signal intensities across multiple image weightings, to perform efficient denoising. Performance was evaluated on synthetic and in-vivo T2- and T1-weighted brain imaging data, and compared to the nonlocal-means (NLM) and its multispectral version, that is, MS-NLM, and the nonlocal maximum likelihood (NLML) filters. Visual inspection of filtered images and quantitative analyses showed that all filters provided substantial reduction of noise. Further, as expected, the use of multispectral information improves filtering quality. In addition, numerical and experimental analyses indicated that the new multispectral NLML filter, MS-NLML, demonstrated markedly less blurring and loss of image detail than seen with the other filters evaluated. In addition, since noise standard deviation (SD) is an important parameter for all of those nonlocal filters, a multispectral extension of the method of maximum likelihood estimation (MLE) of noise amplitude is presented and compared to both local and nonlocal MLE methods. Numerical and experimental analyses indicated the superior performance of this multispectral method for estimation of noise SD.

show abstract

“…Gradient-based filters, such as total variation regularization methods [2], [3] and anisotropic diffusion filters [4], [5], can effectively remove noise in MR images while erasing clinically relevant edges and generating undesired piecewise constant artifacts. Statistic approaches are also widely studied for MR images denoising, including maximum likelihood approaches [6], [7], linear minimum mean square error estimation [8], [9], and the Bayesian estimation method [10]. Another popular technique for the noise reduction of MR images is the transform-based approaches, such as wavelet based method [11], contourlet based method [12] and curvelet based method [13], which have demonstrated the effectiveness of noise removal.…”

Section: Introductionmentioning

confidence: 99%

Denoising 3D Magnetic Resonance Images Based on Low-Rank Tensor Approximation With Adaptive Multirank Estimation

Wang

2019

IEEE Access

View full text Add to dashboard Cite

The magnetic resonance (MR) imaging technique is widely used in clinical diagnosis. Unfortunately, in practice, the MR images inevitably suffer from noise, which severely degrades image quality and accordingly impacts on the accuracy of clinical diagnosis. By exploiting both the nonlocal similarity over space and the inherent correlation across the slices of the 3D MR images, in this paper, we present a novel Rician noise reduction method for the 3D MR images. Specifically, the 3D nonlocal similar patches are first extracted from the input noisy 3D MR data and then grouped to form a noisy fourthorder tensor. Since 3D patches used to construct the fourth-order tensor share similar structures, a latent noise-free tensor can be approximated by a low-rank tensor. To this end, the higher-order singular value decomposition (HOSVD) is adopted to recover the latent noise-free tensor. Furthermore, the rank of each mode of the tensor is adaptively determined by an enhanced low-rank method. The experimental results on synthetic and real 3D MR images show that the proposed method outperforms several state-of-the-art denoising methods in terms of objective metrics and visual inspection. INDEX TERMS 3D image denoising, magnetic resonance image, low-rank tensor approximation, nonlocal similarity, higher-order singular value decomposition.

show abstract

A New Nonlocal Maximum Likelihood Estimation Method for Denoising Magnetic Resonance Images

Cited by 4 publications

References 17 publications

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems

Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter

Denoising 3D Magnetic Resonance Images Based on Low-Rank Tensor Approximation With Adaptive Multirank Estimation

Contact Info

Product

Resources

About