Neighborhood contraction MRI de-noising method based on Chi-square unbiased risk estimation

A technology of partial estimation and neighborhood, applied in the field of MRI denoising with neighborhood shrinkage, to achieve good MRI denoising effect and improve the effect of signal-to-noise ratio

Active Publication Date: 2017-03-01
ZHEJIANG NORMAL UNIVERSITY
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Problems solved by technology

Luisier et al. (2012) in the paper "CURE for noisy Magnetic Resonance Imgaes: Chi-square unbiased riskestimation" for the non-central chi-square distribution model of square MRI, derived the unbiased estimation expression for the mean square error of the chi-square distribution (CURE), applied to two linear threshold extension denoising methods (LET), one is to determine the LET parameters under the UWT transform of the wavelet domain for the CURE estimation of the image domain, and the other is to use the CURE estimation of the wavelet domain to determine the parameters of the wavelet domain The parameters of LET, due to the special nature of the non-central chi-square distribution, the CURE estimation in the wavelet domain can only be applied to the unnormalized Haar (Haar) wavelet transform

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  • Neighborhood contraction MRI de-noising method based on Chi-square unbiased risk estimation
  • Neighborhood contraction MRI de-noising method based on Chi-square unbiased risk estimation
  • Neighborhood contraction MRI de-noising method based on Chi-square unbiased risk estimation

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Embodiment Construction

[0059] The present invention provides a neighborhood shrinking MRI denoising method based on chi-square unbiased estimation. When it is necessary to denoise the noisy MRI, such as figure 1 Perform the following steps in sequence as shown:

[0060] Step 1 Estimate the noise standard deviation σ in the background region of the MRI:

[0061]

[0062] μ is the mean value of the pixel values ​​in the selected background area.

[0063] Step 2 Square the noise image m, and then divide it by the square of the noise standard deviation σ to get the image y;

[0064] y=m 2 / σ 2 (2)

[0065] Step 3 Perform unnormalized stationary Haar wavelet transform on y, decompose the L layer, and obtain high-frequency coefficients and low-frequency coefficients, and the high-frequency coefficients have L*3 subbands;

[0066] Step 4 Use bilateral filter to deblur the low-frequency coefficients; use NeighShrinkCURE method to denoise each subband within the predetermined threshold search range,...

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Abstract

The invention discloses a neighborhood contraction MRI de-noising method based on Chi-square unbiased risk estimation. The method comprises steps of: after estimating the noise standard deviation, squaring a noise image and then dividing the obtained value with the square of the noise standard variation so as to satisfy the property of the noncentral Chi-square distribution; then, carrying out stable Haar wavelet transform which has not been normalized so as to obtain high-frequency coefficient and low-frequency coefficient; deblurring the low-frequency coefficient by use of a bilateral filter; carrying out cyclic shift on the denoised wavelet coefficient on the high-efficient coefficient by use of the neighborhood contraction method based on the Chi-square unbiased risk estimation; and finally, averaging multiple shifted denoising images so as to obtain the denoised image.

Description

technical field [0001] The invention belongs to the technical field of image processing. Specifically, it relates to a neighborhood contraction MRI denoising method based on chi-square unbiased estimation for the purpose of removing MRI Rice noise and improving the signal-to-noise ratio. Background technique [0002] In modern medicine, magnetic resonance imaging (MRI), as a basic medical imaging technology, has become an important auxiliary means for doctors to diagnose and treat. During the acquisition process of magnetic resonance images, noise will be introduced due to factors of hardware circuits and human body. The quality of MRI is reduced, the boundaries of some tissues become blurred, and the fine structures are difficult to distinguish, which increases the difficulty of identifying, analyzing and processing image details, and affects medical diagnosis. Therefore, it is practical, convenient and cost-effective to use software denoising for noisy MRI. [0003] The ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10088G06T2207/20028G06T2207/20064G06T2207/30004
Inventor 张长江黄学优
Owner ZHEJIANG NORMAL UNIVERSITY
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