基于条件生成对抗网络的CT图像去噪方法

CT Image Denoising Method Based on Conditional Generating Countermeasure Network

医学图像在重建过程中总会受到噪声干扰,对于此问题,本文提出了一种基于条件生成对抗网络(CGAN)的去噪方法,算法以完整图像作为网络的输入及输出,使生成的图像信息更加稳定可靠。为了适应CT图像的特点,本文对CGAN结构进行了改进,使其能够适应不同噪声水平下的加性高斯白噪声,为了提高效率,在判别器进行训练时采用了损失判别,且在Tensorflow环境下训练网络模型。实验结果表明,与其他传统图像去噪算法相比,本方法能在保留特征信息的同时有效减少图像中的噪声。

Medical images are always disturbed by noise in the reconstruction process.For this problem,a method of condition generation against network(CGAN)is proposed.The algorithm takes the complete image as the input and output of the network,which makes the generated image information more stable and reliable.In order to adapt to the characteristics of CT image,CGAN structure is improved to adapt to additive Gaussian white noise at different noise levels.In order to improve efficiency,loss discrimination is used in discriminator training,and the network model is trained in tensorflow environment.The experiment results show that compared with other traditional image denoising algorithms,this method can save the feature information and reduce the noise in the image more effectively.