CNN medical CT image denoising method based on noise prior

Abstract

The invention relates to CNN medical CT image denoising based on noise prior. The method comprises the following specific steps: 1), creating a medical CT image model; 2) constructing a noise prior information extraction network; 3) constructing a denoising network; 4) training a noise prior information extraction network and updating parameters; 5) training a training denoising network and updating parameters; 6) denoising the medical CT image; the medical CT image denoising method has the advantages and innovations that denoising is carried out by using noise prior information of the medicalCT image; two networks are used for denoising, wherein the two networks are respectively a noise prior information extraction network and a denoising network; performing the noise information extraction on the CT medical image by the noise prior information extraction network to obtain more image information; serially splicing a noise level diagram predicted by the noise prior information extraction network with the noise diagram, and inputting the noise level diagram and the noise diagram into a denoising network; the BN layer is added to the network, so that the generalization ability of the network is improved, the network convergence speed is increased, and the network denoising performance is improved.

Description

technical field [0001] The present invention relates to the field of medical image denoising, mainly relates to medical CT images, in particular to a noise prior-based CNN medical CT image denoising method suitable for medical CT images. [0002] technical background [0003] Because medical CT images can easily and conveniently obtain patient data information, medical image processing has gradually attracted people's attention. Medical CT images obtained by using various technical means have become an indispensable part of the work and life of medical staff, helping doctors to conduct accurate analysis and comprehensive diagnosis of diseases. With the continuous increase in the amount of patient data currently obtained, the symptoms are gradually showing a trend of diversity, which brings great challenges to patients in making scientific medical diagnosis and accurate treatment. [0004] During the process of digital data acquisition and data transmission, medical CT images...

AI Technical Summary

Problems solved by technology

However, some detailed images will be lost in the image after noise reduction, and more detailed textures will be lost in relatively high noise images. It is also difficult to achieve true multi-level denoising, which does not meet the requirements of denoising. Purpose

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