Medical image brightness homogenization correction method

A medical image and correction method technology, applied in the field of medical imaging, can solve the problems of uneven image brightness, image quality degradation, affecting the diagnosis quality, etc., to achieve the effect of reducing time and resource costs and improving utilization rate

Pending Publication Date: 2019-11-15
ZHONGSHAN HOSPITAL FUDAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a medical image brightness uniform correction method to solve the problem that the image brightness is not uniform in the prior art, resulting in a decrease in image quality and affecting the quality of diagnosis

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  • Medical image brightness homogenization correction method
  • Medical image brightness homogenization correction method

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Embodiment Construction

[0020] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. Advantages and features of the present invention will be apparent from the following description and claims. It should be noted that all the drawings are in a very simplified form and use imprecise scales, and are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention.

[0021] Please refer to figure 1 and figure 2 , figure 1 It is a flow chart of the medical image brightness uniform correction method of the present invention; figure 2 The training process for the generative adversarial neural network. Such as figure 1 and figure 2 As shown, the medical image brightness uniform correction method includes:

[0022] First, step S1 is executed to construct a brightness-deficient image set of the target medical image; wherein, the target medical image is a medical image with...

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Abstract

The invention provides a medical image brightness homogenization correction method. The medical image brightness homogenization correction method comprises the steps: constructing a brightness defectimage set of a target medical image; performing deep convolutional neural network training by taking the target medical image as a standard to obtain an image brightness distribution feature; constructing an image brightness adjustment model according to the image brightness distribution characteristics; and training a to-be-corrected medical image according to the image brightness adjustment model to obtain a medical image with uniform brightness. An image brightness adjustment model (namely, a generative adversarial neural network) is constructed through image brightness distribution characteristics obtained through deep convolutional neural network training, and medical image brightness homogenization correction is automatically performed by utilizing the image brightness adjustment model, and the medical image utilization rate is improved, and the time and resource cost caused by re-scanning is reduced.

Description

technical field [0001] The invention relates to the technical field of medical imaging, in particular to a medical image brightness uniform correction method. Background technique [0002] In medical imaging systems, image quality depends on many factors, such as spatial resolution, tissue contrast, signal-to-noise ratio, contrast-to-noise ratio, and image defects. In order to present the best image quality, hardware and scanning parameters are optimized according to different organs. However, there are still irresistible factors, such as hardware limitations or physiological factors of the subject, which affect the scan and often cannot reach the optimal setting. [0003] One of the biggest defects affecting image quality is image brightness, and uneven image brightness can affect diagnostic quality. For example, in magnetic resonance imaging, non-uniform radio frequency fields can cause uneven image brightness. In order to ensure the quality of image scanning, operators...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/001G06T2207/10088G06T2207/10104G06T2207/20081G06T2207/20084G06T2207/30016
Inventor 石洪成陈曙光顾宇参余浩军修雁李蓓蕾胡鹏程张一秋
Owner ZHONGSHAN HOSPITAL FUDAN UNIV
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