Denoising method for human embryo heart ultrasonic image based on deep convolutional neural network

A deep convolution, ultrasound image technology, applied in the field of image processing, can solve the problems of loss of key image information, optimization, consuming a lot of time and energy, etc., to achieve good prediction of noise distribution, good denoising effect, and improved efficiency.

Pending Publication Date: 2020-08-21
DALIAN UNIV OF TECH
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Benefits of technology

This patented technology allows us to use advanced techniques like Deep Learning (DL) or Convolution Neural Networks (CNN), making it possible to improve the quality of sonographic imaging data by reducing unwanted signals such as background clutter caused during scans. It also saves time because we only require one trained dataset per scan instead of multiple large datasets needed every few months due to its ability to efficiently process high volume data with fewer samples than current methods. Overall this makes processing more efficient and faster compared to existing methods.

Problems solved by technology

This patents discusses different methods used during ultrasonic examinations (ultrasounds) that aim at identifying if there're any signs about how well blood flows from specific parts of the body called the foetuses when they develop into newborn babies. These techniques require expensive equipment with advanced algorithms designed specifically for these types of imaging data. Additionally, existing approaches have limitations such as requiring multiple scans over several hours per patient, resulting in poor results.

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  • Denoising method for human embryo heart ultrasonic image based on deep convolutional neural network
  • Denoising method for human embryo heart ultrasonic image based on deep convolutional neural network
  • Denoising method for human embryo heart ultrasonic image based on deep convolutional neural network

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Embodiment Construction

[0038] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention:

[0039] Such as figure 1 A denoising method for human embryonic heart ultrasound images based on a deep convolutional neural network is shown, and the specific scheme is:

[0040] S1: Acquire ultrasonic target images of time series and space series and select the central image, and select the adjacent images of the central image, such as figure 2 shown;

[0041] S11: converting the ultrasonic image contained in the case data into a grayscale image;

[0042] S12: Select an image as the center image, the original image of the center image is as follows image 3 As shown in , a total of 4 adjacent images in the time sequence and two adjacent images in the spatial sequence are obtained as adjace

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Abstract

The invention discloses a denoising method for a human embryo heart ultrasonic image based on a deep convolutional neural network, and the method comprises the following steps: obtaining an ultrasonicimage data set with time sequence and space sequence features, selecting a central image, and determining an adjacent image of the central image; calculating the similarity between the central pixelof the central image and each pixel in the search domain corresponding to the adjacent image; calculating a central pixel gray value corresponding to the adjacent image according to the similarity information and averaging the central pixel gray value to obtain a final gray value of the central pixel; and constructing a deep convolutional neural network model, and inputting the ultrasonic image which is not denoised into the trained deep convolutional neural network model to carry out denoising processing to obtain a difference between the noise image and the clean image, namely a residual image.

Description

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Claims

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Application Information

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Owner DALIAN UNIV OF TECH
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