CT image denoising method based on wavelet transformation

A technology of CT image and wavelet transformation, which is applied in the field of medical image processing and image signal processing, can solve problems such as CT image interference, and achieve the effect of no fixed deviation, obvious shrinkage effect, and good adaptability

Active Publication Date: 2021-08-06
BEIJING INSTITUTE OF TECHNOLOGYGY +2
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Problems solved by technology

CT images are interfered by Gaussian no

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  • CT image denoising method based on wavelet transformation
  • CT image denoising method based on wavelet transformation
  • CT image denoising method based on wavelet transformation

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[0051] The present invention will be described in detail below according to the drawings and examples, but specific embodiments of the invention are not only here.

[0052] This example describes the denoising process used in the present invention to be used in Gaussian noise of new crown pneumonia chest. Embodiments of the present invention will be specifically described below with reference to the accompanying drawings.

[0053] figure 1 For the overall flow chart of the algorithm of the present invention, a wavelet transform CT image denoising method contains the following steps:

[0054] Step A. Select the DB5 wavelet basis function to wavelet decomposition of the CT image containing Gaussian noise.

[0055] Step B, multi-scale wavelet transform of the wavelet decomposition coefficient of the noiseable image. After the wavelet decomposes, different subband coefficients are obtained: low frequency components (including horizontal detail components, vertical detail components, ...

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Abstract

The invention discloses a CT image denoising method based on wavelet transformation, belongs to the technical field of medical image processing, and is particularly suitable for CT image denoising of new crown pneumonia. The CT image is susceptible to the interference of Gaussian noise in the transmission and acquisition process, and the wavelet transform can effectively remove the interference of the Gaussian noise. In order to solve the problems that early-stage lesions of a new crown CT image are not obvious in change, the number of the lesions is small, the range of the lesions is small, the density is low, and missed diagnosis of early-stage new crown patients is easily caused, the contrast ratio of the new crown lesions is improved, namely, an arc tangent improved self-adaptive wavelet threshold function of index adjustment and an improved threshold based on contraction factors are provided; the arc tangent function changes quickly near the zero point and changes slowly away from the zero point, the exponential function is adjusted to adapt to different layer threshold functions, more high-frequency detail information in the lung CT image is obtained, the detail edge is reserved, and fuzziness is reduced. The selection of wavelet threshold function parameters is a key factor for determining distortion and errors after image denoising, and the optimal adjustment parameters are searched through the improved particle swarm optimization of sine and cosine fusion normal distribution, so that the threshold optimization effect is greatly improved.

Description

technical field [0001] The invention relates to the technical field of image signal processing, in particular to a wavelet transform CT image denoising method based on an improved threshold function, which is suitable for filtering and processing of pneumonia CT images, especially suitable for the denoising of CT images of new coronary pneumonia, and belongs to medical image processing technology field. Background technique [0002] The lesions of COVID-19 are characterized by various forms of ground-glass opacity, or accompanied by consolidation. CT images are disturbed by Gaussian noise during transmission and acquisition. Digital image wavelet transform is a new method of processing images using mathematical methods. It is a problem of numerical approximation. The parent function is expanded and translated to a new function space, and the best approximation method is found according to the criteria, so as to realize the original image information and The distinction of ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/10G06N3/00
CPCG06T5/002G06T5/10G06N3/006G06T2207/10081G06T2207/20064G06T2207/20081G06T2207/30061
Inventor 郭树理王国威韩丽娜宋晓伟杨文涛
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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