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A Combinational De-Noising Algorithm for Low-Dose Computed Tomography

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Advances in Image and Graphics Technologies (IGTA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 757))

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Abstract

To improve the image quality of low-dose CT, this paper proposes a modified algorithm which combined with the projection domain de-noising and reference-based non-local means (RNLM) filtering in the image domain. A generalized Anscombe transformation (GAT) is used to improve the effectiveness of the stabilization and filtering. The exact unbiased inverse of the GAT is also applied to ensure accurate de-noising results. The experimental results demonstrate that the proposed method could significantly improve the quality and preserve the edges of low-dose CT images.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61372014) and the Scientific and Technological Development Plan Program of Siping (No. 2016062).

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Authors and Affiliations

  1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China

    Wei Zhang & Yan Kang

  2. Computer School, Jilin Normal University, Siping, China

    Wei Zhang

Authors
  1. Wei Zhang
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  2. Yan Kang
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Corresponding author

Correspondence to Yan Kang .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Tsinghua University, Beijing, China

    Shengjin Wang

  3. Beijing Institute of Technology, Beijing, China

    Yue Liu

  4. Beijing Institute of Technology, Beijing, China

    Jian Yang

  5. School of EECS, Center for Information Science, Peking University, Beijing, China

    Xiaoru Yuan

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  7. La Trobe University, Melbourne, Victoria, Australia

    Henry Been-Lirn Duh

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Zhang, W., Kang, Y. (2018). A Combinational De-Noising Algorithm for Low-Dose Computed Tomography. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_26

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  • DOI: https://doi.org/10.1007/978-981-10-7389-2_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7388-5

  • Online ISBN: 978-981-10-7389-2

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