Low-dose CT tooth image denoising method based on double-residual network

A low-dose, double-residual technology, applied in the field of image denoising, which can solve the problems of high noise, recovery of tiny details to be improved, and low definition.

Active Publication Date: 2020-06-19
LIAONING NORMAL UNIVERSITY
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Problems solved by technology

However, most of the denoising methods based on deep learning use a single neural network to achieve denoising tasks, but the ability to restore tiny details needs to be improved, and the fine texture of

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  • Low-dose CT tooth image denoising method based on double-residual network
  • Low-dose CT tooth image denoising method based on double-residual network
  • Low-dose CT tooth image denoising method based on double-residual network

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Embodiment Construction

[0041] A low-dose CT dental image denoising method based on a double residual network of the present invention is as follows: figure 1 As shown, follow the steps below:

[0042] In part 01, the grayscale image of the BSD500 data set is taken, and the preprocessing operation is performed before entering the network model. The specific steps are as follows:

[0043] Step C011: Import the grayscale images of the BSD500 dataset, which are 500 preprocessed images and 500 real images respectively. Among the 500 grayscale images, 432 are used as the training set and 68 are used as the test set. is Pre_Image, and the real image data set used for verification is recorded as Real_Image; 500 preprocessed images are respectively recorded as Pre_Image1, Pre_Image2,..., Pre_Image500; 500 real images are respectively recorded as Real_Image1, Real_Image2,..., Real_Image500;

[0044] Step C012: Cut 500 pre-processed images and 500 real images into blocks, the size of which is 48*48 pixels, an...

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Abstract

The invention discloses a low-dose CT tooth image denoising method based on a double-residual network, and the method comprises the steps: firstly training an auxiliary network, and storing a model; and calculating a loss function value by utilizing a similarity relationship between the feature space diagrams extracted by the double-residual network, and updating parameters by combining the loss function value between the denoising network and the double-residual network so as to assist the training of the denoising network. The method mainly comprises a data loading module, an auxiliary network training module, a double-residual network denoising module and a testing module. The low-dose CT tooth image denoising method has a remarkable effect on low-dose CT tooth image denoising, can reserve some detail features of the image, and has important application value.

Description

technical field [0001] The method of the invention relates to an image denoising method, in particular to a low-dose CT tooth image denoising method based on a double residual network. Background technique [0002] In recent years, oral CT imaging technology has been more and more widely used in the diagnosis and treatment of oral cavity and teeth. Oral CT uses the acquisition instrument to collect data spirally around the human tissue area to be detected, and can obtain tomographic images in various directions, which can then be used to assist in the measurement before dental surgery, oral inflammation, tumors and other oral diseases. The radiation dose of low-dose CT equipment to the human body is about 1 / 5 of that of traditional CT scanners. Therefore, the use of low-dose equipment can reduce radiation dose, reduce unnecessary psychological burden for patients, and allow patients to receive CT scans with more peace of mind. However, when choosing a reduced-dose CT scan, ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/00
CPCG06T5/002G06T7/0012G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30036
Inventor 傅博王丽妍杜飞飞刘芳菲
Owner LIAONING NORMAL UNIVERSITY
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