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Low-dose CT (Computed Tomography) image denoising method and system based on gradient guidance and self-similarity correction

A self-similarity, CT image technology, applied in the field of image processing, can solve the problems of distortion, denoising image texture details, smooth structure, large computing resource consumption, etc., achieve simple and accurate calculation, alleviate model parameter redundancy, and improve utilization. rate effect

Pending Publication Date: 2021-12-31
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the problems of high computational resource consumption, over-smoothness of denoising image texture details and structural distortion existing in the prior art, and provide a gradient gradient method for fully mining low-dose CT image information and effectively extracting image features. Low-dose CT image denoising method and system with guidance and self-similarity correction

Method used

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  • Low-dose CT (Computed Tomography) image denoising method and system based on gradient guidance and self-similarity correction
  • Low-dose CT (Computed Tomography) image denoising method and system based on gradient guidance and self-similarity correction
  • Low-dose CT (Computed Tomography) image denoising method and system based on gradient guidance and self-similarity correction

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Embodiment 1

[0046] Such as figure 1 As shown, the present embodiment provides a low-dose CT image denoising method guided by gradient and self-similarity correction, comprising the following steps:

[0047] S1. Obtain the noisy gradient image of the first image according to the gradient extraction operator, which is denoted as the first noisy gradient image; extract the shallow features of the first image and the first noisy gradient image, respectively denoted as the shallow layer of the first image features and first gradient shallow features;

[0048] S2, performing m times of gradient guidance and self-similarity correction;

[0049] Among them, the steps of each gradient guidance and self-similar correction are as follows:

[0050] Input the first image feature and the first gradient feature; if the gradient guidance and self-similar correction are performed for the first time, the first image feature is the first image shallow feature; the first gradient feature is the first gradi...

Embodiment 2

[0074] This embodiment describes in detail the gradient-guided and self-similarity-corrected low-dose CT image denoising method described in Embodiment 1 based on the application field of low-dose CT images. The network structure diagram of the denoising method is as follows figure 2 As shown, the size of the convolution kernel except the last layer is 3×3, the size of the convolution kernel of the last layer is 1×1, the encoding module is composed of a large-step convolution layer, and the decoding module is a deconvolution layer. The flowchart of the denoising method is shown in image 3 As shown, the specific steps are as follows:

[0075] Specifically, in step S1, the first image is a low-dose CT image; the low-dose CT image is expressed as Where H is the image height and W is the image width. build as Figure 4 The gradient extraction operator shown is used to obtain low-dose CT images according to the gradient extraction operator The noisy gradient image of is de...

Embodiment 3

[0110] Such as Figure 8 As shown, this embodiment provides a low-dose CT image denoising system guided by gradient and self-similarity correction, including a first image shallow feature extraction module, a plurality of first components, a feature reconstruction module, a gradient extraction module, The first gradient shallow feature extraction module, multiple second components and gradient feature reconstruction module; the first image is respectively input to the gradient extraction module and the first image shallow feature extraction module; the gradient extraction module is extracted through the first gradient shallow feature extraction module, a plurality of second components are connected to the gradient feature reconstruction module; the second component includes the first gradient encoding module and the first gradient decoding module connected in sequence; the first image shallow feature extraction module is passed through a plurality of first A component is conne...

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Abstract

The invention discloses a low-dose CT (Computed Tomography) image denoising method and system based on gradient guidance and self-similarity correction. The method comprises the following steps: S1, obtaining a noisy gradient image of a first image according to a gradient extraction operator, and recording the noisy gradient image as a first noisy gradient image; extracting shallow layer features of the first image and the first noise-containing gradient image, and respectively recording the shallow layer features as first image shallow layer features and first gradient shallow layer features; S2, performing m times of gradient guidance and self-similarity correction; and S3, reconstructing a third image feature obtained by the last gradient guidance and self-similarity correction into a second image consistent with the first image in size, and outputting the second image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a low-dose CT image denoising method and system guided by gradient and corrected by self-similarity. Background technique [0002] X-ray computed tomography (Computed Tomography, CT), as a non-invasive human internal tissue structure imaging technology, has been widely used in clinical disease diagnosis. However, with the increasing use of CT scanning year by year, the problem of X-ray radiation in the scanning process has attracted widespread attention. Investigations and studies have found that excessive CT scanning radiation can induce various diseases and even increase the risk of cancer, but reducing the radiation dose will also reduce the signal-to-noise ratio of CT images, that is, there will be obvious noise and artifacts in CT images. It may seriously affect the doctor's diagnosis of the disease. Therefore, how to provide high-quality image data for disease diagnosis wh...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06N3/045G06T5/70Y02T10/40
Inventor 江雪聪何中市王路路杜井龙伍星
Owner CHONGQING UNIV
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