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Digital X-ray image denoising method based on trainable joint bilateral filter

A bilateral filter and X-ray technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problem of reducing image quality, appearance, texture and other details that are difficult to substantially improve, and affect the follow-up processing of DR images, etc. problem, to achieve a robust effect

Pending Publication Date: 2022-05-13
SOUTHEAST UNIV
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Problems solved by technology

However, there is an obvious positive correlation between imaging quality and radiation dose, and the reduction of radiation dose usually introduces noise and artifacts, reducing the imaging quality; in addition, these noises will also affect the subsequent processing of DR images, such as image segmentation, recognition Wait
[0003] Commonly used DR image denoising methods, such as least squares filtering, mean filtering, and bilateral filtering, can maintain the overall quality of the image under low-dose conditions, but it is difficult to substantially improve the details of local appearance and texture.

Method used

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  • Digital X-ray image denoising method based on trainable joint bilateral filter
  • Digital X-ray image denoising method based on trainable joint bilateral filter
  • Digital X-ray image denoising method based on trainable joint bilateral filter

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Embodiment 1

[0035] Example 1: see figure 1 , a digital X-ray image denoising method based on a trainable joint bilateral filter, the specific steps are as follows:

[0036] Step 1. Randomly read 128×128 low-dose digital X-ray images as the input of the EDCNN network, and the corresponding normal dose images as labels, and jointly train the EDCNN network.

[0037] Step 2, use the trained EDCNN network to initially remove the noise in the image, and generate a guide image for training the parameters of the joint bilateral filter.

[0038] Specifically, the EDCNN network consists of 1 edge enhancement module and 8 convolutional blocks, such as figure 2 shown. Among them, the convolution block is composed of the order of "1*1 convolution + LeakyReLU activation layer + 3*3 convolution + LeakyReLU activation layer", such as image 3 shown. The edge enhancement module consists of 4 Sobel operators in vertical, horizontal and 2 diagonal directions, namely:

[0039]

[0040] The EDCNN net...

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Abstract

The invention discloses a plug-and-play and trainable combined bilateral filtering processing method which is used for low-dose digital X-ray imaging. Firstly, a guide image of the joint bilateral filter is generated through a deep neural network, then the obtained guide image and an original noise image are used for jointly training the joint bilateral filter, and finally a denoised image is obtained. According to the invention, under the condition that the X-ray dose is greatly reduced, a high-quality image can be quickly generated, and an effective denoising method is provided for a low-dose digital X-ray image.

Description

technical field [0001] The invention relates to a digital X-ray image denoising method based on a trainable joint bilateral filter, which belongs to the field of computer image processing. Background technique [0002] Digital X-ray imaging (DR) is an advanced X-ray photography technology formed by the combination of computer digital image processing technology and X-ray radiation technology. X-rays belong to ionizing radiation, and its excessive or inappropriate use may also damage normal organs. Rationalization of the use of X-ray imaging and optimization of the associated radiation doses are essential to protect the health of patients. However, there is an obvious positive correlation between imaging quality and radiation dose, and the reduction of radiation dose usually introduces noise and artifacts, reducing the imaging quality; in addition, these noises will also affect the subsequent processing of DR images, such as image segmentation, recognition Wait. [0003] C...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04
CPCG06T2207/10116G06T2207/20028G06N3/045G06T5/70
Inventor 陈阳吴钱御
Owner SOUTHEAST UNIV
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