小波各向异性模型肺部CT图像去噪

摘要

在获取肺部CT图像过程中不可避免地要受到噪声污染, 使用传统的去噪算法不能在对肺部CT图像有效去噪的同时很好地保持边缘、纹理等有用信息.为在肺部CT图像去噪时很好地保持边缘、纹理等细节信息, 提出一种新的小波各向异性模型肺部CT图像去噪算法.算法首先对含噪的肺部CT图像进行Daubechies小波 (dbN) 软阈值去噪, 然后在此基础上利用各向异性模型去噪.实验结果表明, 与传统去噪算法相比, 所提算法不仅去噪后的肺部CT图像噪声点较少而且具有更好的边缘、纹理等细节信息保持性.%Noise pollution on pulmonary CT images is always unavoidable during the acqisition of the images, traditional denoising algorithm can't successfully get rid of noise on pulmonary CT images effectively without destroying the texture and edge features. In order to filter noise of pulmonary CT images and keep the edge and texture signal in images, pulmonary CT Image denoising algorithm based on wavelet anisotropic model is presented. First, algorithm carries on Daubechies (dbN) wavelet soft threshold denoising to noisy pulmonary CT image. Then on this basis, algorithm denoise using anisotropic model. Experimental results showthat this method has better effect for keeping edge and visual smooth, compared with traditional denoising method.