A chest X-ray film denoising method based on a deep convolutional neural network

A convolutional neural network and neural network technology, which is applied in the field of chest X-ray denoising based on deep convolutional neural network, can solve the problems of insufficient model robustness and generalization ability, achieve fast calculation speed and improve practicality good denoising effect

Inactive Publication Date: 2019-06-28
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] Denoising autoencoders and convolutional denoising autoencoders (CNN DAE) are extensions of classic autoencoders. Both models try to learn clean denoised pictures

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  • A chest X-ray film denoising method based on a deep convolutional neural network
  • A chest X-ray film denoising method based on a deep convolutional neural network
  • A chest X-ray film denoising method based on a deep convolutional neural network

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Embodiment 1

[0052] The present invention provides a method for denoising a chest X-ray film based on a deep convolutional neural network, comprising the following steps:

[0053] Step 1: Collect chest X-ray data, convert the data format, and obtain the original image block after preprocessing. Gaussian noise is added to the original image block to generate a noisy image, and the paired original image block and noisy image are used as training data set.

[0054] Step 11: convert the image in the IMG format in the chest X-ray film dataset into the JPG data format;

[0055] The dataset used in the present invention is nodular and non-nodular chest radiographic images provided by the Japanese Society of Radiological Technology Committee (JSRT). This is a publicly available dataset of 247 chest X-ray images collected from 13 institutions in Japan and one in the United States. For privacy protection, the IMG data format is used for encryption. The present invention uses Image J software to s...

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Abstract

The invention discloses a chest X-ray film denoising method based on a deep convolutional neural network. The method comprises the steps of collecting chest X-ray film data, performing data format conversion, performing preprocessing to obtain an original image block for training, adding Gaussian noise to generate a noisy image block, and taking the paired original image block and noisy image block as a training data set; Constructing a convolutional neural network model for removing chest X-ray film noise, wherein the convolutional neural network model comprises a deep convolutional neural network and a residual error network; Taking the paired original image blocks and noisy image blocks as input, and performing training to obtain a trained convolutional neural network model X-ReCNN; taking the chest X-ray data of the noise to be removed as an input characteristic map of X-ReCNN, removing noise, and outputting the predicted denoised chest X-ray film.. According to the method, the noise in the chest X-ray film can be removed with light weight, high speed and high precision, the parameters of a network structure are greatly reduced, and the network training time is shortened.

Description

technical field [0001] The invention belongs to the technical field of image denoising, and in particular relates to a method for denoising a chest X-ray film based on a deep convolutional neural network. Background technique [0002] Chest X-ray is an inexpensive, fast and easy-to-obtain medical imaging technique. Compared with computed tomography (CT), traditional chest X-rays are not only cheaper, but also have lower radiation dose. Now medical research shows that 1.5% to 2% of tumors may be due to high radiation from CT Dosage causes. Especially in the early diagnosis of diseases such as pneumonia, pneumothorax, interstitial lung disease, heart failure, fracture, hiatal hernia, etc., X-rays are widely used. In addition, chest X-ray examination is the standard examination method for more than 300 million people in the country in 2014. This number is still increasing, resulting in hundreds of millions of chest X-rays being produced every year. [0003] Although chest X...

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Application Information

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IPC IPC(8): G06T5/00G06T3/40G06T3/60G06N3/04
Inventor 金燕蒋晓奔韦振坤李远姚宇
Owner ZHEJIANG UNIV OF TECH
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