东南大学学报（自然科学版）2012，Vol.42Issue(5)：864-868,5.DOI:10.3969/j.issn.1001-0505.2012.05.013
基于字典学习的低剂量X-ray CT图像去噪
Dictionary learning based denoising of low-dose X-ray CT image
朱永成 1陈阳 1罗立民 2Toumoulin Christine3
作者信息
- 1. 东南大学影像科学与技术实验室,南京210096
- 2. 中法生物医学信息研究中心,法国雷恩35000
- 3. 法国雷恩大学信号与图像处理实验室,法国雷恩35042
- 折叠
摘要
Abstract
A dictionary learning based denoising method is introduced to eliminate the noise in low-dose computed-tomography (LDCT) image. Aiming at the phantom and patient images, the &-means singular value decomposition (K-SVD) algorithm is adopted to train image dictionary itera-tively based on LDCT and normal-dose CT (NDCT) images. Then, based on the orthogonal matching pursuit algorithm, the sparse representation decomposes the noise image into sparse component which is related to image information and remains which are regarded as noise. Finally, noises can be suppressed by reconstructing image only with its sparse components. The experimental results show that the performance of the proposed method is strongly affected by the dictionary size and the constraints for sparsity in dictionary training. The better performance can be achieved when training samples are extracted from NDCT image instead of LDCT image. Under the same noise level, compared with the traditional de-noising methods, the proposed method is more effective in suppressing noise and improving the visual effect while maintaining more diagnostic image details.关键词
K-SVD算法/低剂量CT/字典学习/稀疏表示Key words
k-means singular value decomposition algorithm/ low-dose computed-tomography/ learning dictionary/ sparse representation分类
信息技术与安全科学引用本文复制引用
朱永成,陈阳,罗立民,Toumoulin Christine..基于字典学习的低剂量X-ray CT图像去噪[J].东南大学学报（自然科学版）,2012,42(5):864-868,5.
