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  • 标题:Denoising CT Images using wavelet transform
  • 本地全文:下载
  • 作者:Lubna Gabralla ; Hela Mahersia ; Marwan Zaroug
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2015
  • 卷号:6
  • 期号:5
  • DOI:10.14569/IJACSA.2015.060520
  • 出版社:Science and Information Society (SAI)
  • 摘要:Image denoising is one of the most significant tasks especially in medical image processing, where the original images are of poor quality due the noises and artifacts introduces by the acquisition systems. In this paper, we propose a new image denoising scheme by modifying the wavelet coefficients using soft-thresholding method, we present a comparative study of different wavelet denoising techniques for CT images and we discuss the obtained results. The denoising process rejects noise by thresholding in the wavelet domain. The performance is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Finally, Gaussian filter provides better PSNR and lower MSE values. Hence, we conclude that this filter is an efficient one for preprocessing medical images.
  • 关键词:thesai; IJACSA; thesai.org; journal; IJACSA papers; Computed Tomography; Discrete wavelet transform; Lung cancer; Thresholding
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