A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Alexander, Murray E.; Baumgartner, Richard N.; Summers, Arthur R.; Windischberger, Christian; Klarhoefer, Markus; Moser, Ewald; Somorjai, R. L.

doi:10.1016/s0730-725x(99)00128-9

Cited by 87 publications

(33 citation statements)

References 13 publications

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“…Denoising can be applied to the real and imaginary channels, respectively, rather than to the magnitude images. This technique has proved to be effective [11]- [13]. In view of this, the additive Gaussian white noise model is adopted in this paper.…”

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confidence: 99%

Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Bao

Zhang

2003

IEEE Trans. Med. Imaging

258

134

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Abstract-Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.

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confidence: 99%

Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Bao

Zhang

2003

IEEE Trans. Med. Imaging

258

134

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“…To sum up, noise has great influence on various processes of hydrologic series analysis [3,[32][33][34][35], such as the periods' identification, parameters estimation and hydrologic series prediction, and the calculation of entropy values is not a exception as discussed above. Here, the authors suggest the wavelet threshold de-noising method proposed in [26,27] be used in practice, because by using it not only the appropriate mother wavelet can be chosen, but also reliable de-noising results of hydrologic series data can correspondingly be obtained, based on which the analytic results of complexity of hydrologic series data can be improved.…”

Section: Analysis Of Influence Of Noisementioning

confidence: 99%

Wavelet-Based Analysis on the Complexity of Hydrologic Series Data under Multi-Temporal Scales

Sang

Wang

et al. 2011

Entropy

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Abstract:In this paper, the influence of four key issues on wavelet-based analysis of hydrologic series' complexity under multi-temporal scales, including the choice of mother wavelet, noise, estimation of probability density function and trend of series data, was first studied. Then, the complexities of several representative hydrologic series data were quantified and described, based on which the performances of four wavelet-based entropy measures used commonly, namely continuous wavelet entropy (CWE), continuous wavelet relative entropy (CWRE), discrete wavelet entropy (DWE) and discrete wavelet relative entropy (DWRE) respectively, were compared and discussed. Finally, according to the analytic results of various examples, some understanding and conclusions about the calculation of wavelet-based entropy values gained in this study have been summarized, and the corresponding suggestions have also been proposed, based on which the analytic results of complexity of hydrologic series data can be improved.

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“…Th ϭ ͱ2 log e ͑N log 2 ͑N͒͒ [13] where Th is the threshold level; N is the number of signal samples; and is the standard deviation of the noise, estimated as the median of the finest scale coefficients divided by 0.6745. 2.…”

Section: Wsd Analysismentioning

confidence: 99%

Signal de‐noising in magnetic resonance spectroscopy using wavelet transforms

Cancino-De-Greiff

Ramos-Garcia

Lorenzo-Ginori

2002

Concepts Magn. Reson.

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ABSTRACT:Computer signal processing is used for quantitative data analysis (QDA) in magnetic resonance spectroscopy (MRS). The main difficulty in QDA is that MRS signals appear to be contaminated with random noise. Noise reduction can be achieved by coherent averaging, but it is not always possible to average many MRS waveforms. Wavelet shrinkage de-noising (WSD) is a technique that can be employed in this case. The potentialities of WSD in MRS, alone and combined with the Cadzow algorithm, are analyzed through computer simulations. The results can facilitate an appropriate application of WSD, as well as a deeper understanding of this technique.

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A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Cited by 87 publications

References 13 publications

Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Wavelet-Based Analysis on the Complexity of Hydrologic Series Data under Multi-Temporal Scales

Signal de‐noising in magnetic resonance spectroscopy using wavelet transforms

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