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Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method

A non-local average and CT imaging technology, applied in image enhancement, image data processing, instruments, etc., can solve the problems of inaccurate weight calculation, low image resolution, slow processing speed, etc., to improve denoising performance, Good restoration of the original image, accurate calculation of the effect

Inactive Publication Date: 2013-05-22
CHONGQING UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

But it also has the following shortcomings: 1. The weight calculation is not accurate enough; 2. The processing speed is relatively slow; 3. The image resolution is not high

Method used

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  • Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method
  • Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method
  • Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method

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Embodiment 1

[0044] figure 1 is a flowchart of the present invention, figure 2 is the projected image used in the present invention, image 3 is the reconstructed image used in the present invention, Figure 4 is the noisy projected image used in the present invention, Figure 5 is the noisy projection reconstruction image used in the present invention, Image 6 is the reconstructed image after denoising with the current non-local mean filtering method, Figure 7 It is the reconstructed image after denoising by the method of the present invention, as shown in the figure: the noise-containing projection reconstructed image neighborhood plus window non-local mean denoising method provided by the present invention comprises the following steps:

[0045] Step 1. Obtain the original CT projection image data, and the noise in the image is usually dominated by Gaussian additive white noise;

[0046] Step 2. For the parameter t (the size of the pixel image block to be corrected), (Gaussian...

Embodiment 2

[0061] The difference between this embodiment and embodiment 1 is only:

[0062] This embodiment specifically illustrates the process of processing projection image data by the non-local mean CT imaging denoising method based on neighborhood windowing provided by the present invention:

[0063] Obtain raw CT projection image data such as figure 2 , Figure 4 is true figure 2 Add noisy projection image data with a noise standard deviation of 0.1. image 3 is true figure 2 The reconstructed image, Figure 5 is true Figure 4 The reconstructed image.

[0064] Under the above experimental conditions, direct reconstruction, current non-local mean filter denoising reconstruction and the method of the present invention are respectively used to denoise and reconstruct projection data.

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Abstract

The invention discloses a neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method for mainly improving original CT projected image data by adopting a search region and weight calculation in a non-local mean value technology. The main implementation process of the neighborhood windowing based non-local mean value CT imaging de-noising method comprises: (1) setting a mean square error of a Gaussian window for a collected original CT projected image; (2) finding all the similar blocks in the search region; (3) calculating a Gaussian Euclidean distance between the similar blocks and a block where a current spot locates; (4) calculating a similarity weight by utilizing a negative index function, wherein the level of similarity increases along with the increase of the weight; (5) obtaining a product of the similarity weight and a distance weight to obtain a mixed weight; (6) performing weighted average on all the pixel point values in the search region by using the mixed weight to obtain a corrected pixel point value; and (7) reconstructing de-noised projection image data to form a final chromatographic X-ray image. By the adoption of the neighborhood windowing based non-local mean value CT imaging de-noising method disclosed by the invention, the purpose of restoring the original image better and improving the de-noising performances for the image can be realized.

Description

technical field [0001] The invention belongs to the technical field of CT imaging, and relates to a non-local mean value CT imaging denoising method based on neighborhood windowing. Background technique [0002] With the development of science and technology, the application of CT technology has become more and more popular. CT detection plays an important role in people's daily life and scientific research and production. However, in the process of CT imaging, it is often disturbed by unavoidable noise. Since the noise is random and irregular, Gaussian noise is usually the most noise in the imaging process. [0003] CT imaging denoising can be performed before reconstruction or after reconstruction. Denoising before reconstruction can make the noise diffuse without reconstruction. Denoising before CT imaging is to denoise the projection data. [0004] There are many known denoising techniques in modern technology. Traditional image denoising methods include Gaussian filter...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 王珏罗姗邹永宁
Owner CHONGQING UNIV
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