Convolutional neural network medical CT image denoising method based on residual error learning

A convolutional neural network and CT image technology, applied in the field of medical image denoising, to achieve the effect of improving image denoising ability, good pertinence, and improving training efficiency

Active Publication Date: 2019-07-05
ZHEJIANG UNIV OF TECH
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Problems solved by technology

A convolutional neural network medical CT image denoising method based on residual learning is proposed to solve the problem of medical CT image denoising

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  • Convolutional neural network medical CT image denoising method based on residual error learning
  • Convolutional neural network medical CT image denoising method based on residual error learning
  • Convolutional neural network medical CT image denoising method based on residual error learning

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Embodiment Construction

[0065] The invention will be specifically explained below in conjunction with the drawings

[0066] The specific steps of the convolutional neural network medical CT image denoising method based on residual learning of the present invention are as follows:

[0067] Step 1) Construct a medical CT image model;

[0068] The CT image model is mainly composed of two parts, both the effective human tissue reflection signal and the invalid noise signal, while the noise signal includes multiplicative noise and additive noise. Among them, additive noise is more important to CT images than multiplicative noise. The impact is very small. Due to the consideration of multiplicative noise, the general model s(x,y) of CT electrical signal is expressed as:

[0069] s(x,y)=r(x,y)n(x,y) (1)

[0070] Among them, (x, y) represents the horizontal and vertical coordinates of the image, r(x, y) represents the noise-free signal, and n(x, y) represents the multiplying noise.

[0071] Step 2) Construct a neural...

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Abstract

The invention discloses a convolutional neural network medical CT image denoising method based on residual error learning. The method comprises the following specific steps: 1) constructing a medicalCT image model; 2) constructing a neural network model; 3) training the network; 4) updating parameters; and 5) denoising the medical CT image: inputting the medical CT image containing the noise intothe constructed network model, and outputting the medical CT image without the noise through the network. The method has the advantages that medical CT image denoising is carried out by combining knowledge in the aspect of the convolutional neural network in deep learning. Noise in the image is approximately learned in a residual error learning mode, good pertinence is achieved, and meanwhile thetraining efficiency of the neural network is improved. The convolutional neural network and the residual error learning method are adopted, so that the feature information in the image can be betterlearned, and more local image information can be reserved in the image denoising process. And meanwhile, the image denoising capability is also improved.

Description

Technical field [0001] The invention relates to the field of medical image denoising, in particular to medical CT images, in particular to a convolutional neural network medical CT image denoising method based on residual learning suitable for medical CT images. [0002] technical background [0003] With the development of technology, there has been a certain development in the field of medical imaging. For example, imaging technologies such as ultrasound imaging, CT, MRI are widely used in medical clinical diagnosis. Computed Tomography (Computed Tomography, also known as "Computed Tomography", CT for short) is a medical imaging diagnostic examination. This technology was once called Computed Axial Tomography. Computed tomography, using precisely collimated X-ray beams, Y-rays, ultrasound, etc., together with a highly sensitive detector, surrounds a certain part of the human body as a tomographic scan one by one. It has the characteristics of fast scanning time and clear images. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10081G06T2207/20081G06T2207/20084
Inventor 张聚周海林吕金城陈坚
Owner ZHEJIANG UNIV OF TECH
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