Feng Fuqiang, Wang Jun
Image Processing and Image Communications Key Lab., College of Telecommunications & Information Engineering, Nanjing Univ. of Posts & Telecomm, Nanjing 210003, China.
Image Processing and Image Communications Key Lab., College of Geo & Bio Information, Nanjing Univ. of Posts & Telecomm, Nanjing 210003, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013 Oct;30(5):932-5.
Because of various effects of the imaging mechanism, noises are inevitably introduced in medical CT imaging process. Noises in the images will greatly degrade the quality of images and bring difficulties to clinical diagnosis. This paper presents a new method to improve singular value decomposition (SVD) filtering performance in CT image. Filter based on SVD can effectively analyze characteristics of the image in horizontal (and/or vertical) directions. According to the features of CT image, we can make use of discrete cosine transform (DCT) to extract the region of interest and to shield uninterested region so as to realize the extraction of structure characteristics of the image. Then we transformed SVD to the image after DCT, constructing weighting function for image reconstruction adaptively weighted. The algorithm for the novel denoising approach in this paper was applied in CT image denoising, and the experimental results showed that the new method could effectively improve the performance of SVD filtering.
由于成像机制的各种影响，在医学CT成像过程中不可避免地会引入噪声。图像中的噪声会极大地降低图像质量，并给临床诊断带来困难。本文提出了一种提高CT图像中奇异值分解（SVD）滤波性能的新方法。基于SVD的滤波器可以有效地分析图像在水平（和/或垂直）方向上的特征。根据CT图像的特点，我们可以利用离散余弦变换（DCT）来提取感兴趣区域并屏蔽不感兴趣区域，从而实现图像结构特征的提取。然后将SVD应用于DCT后的图像，自适应加权构建用于图像重建的加权函数。本文提出的新型去噪方法算法应用于CT图像去噪，实验结果表明该新方法能有效提高SVD滤波性能。
