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Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

PRCV 2020: Pattern Recognition and Computer Vision pp 3–13Cite as

Medical CT Image Super-Resolution via Cyclic Feature Concentration Network

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12305))

Abstract

Clear and high-resolution medical computed tomography (CT) images are of great significance for early medical diagnosis. In practice, there are many cases of medical CT images that lead to poor image quality which complicates the diagnosis process. This paper proposes a new type of neural network that enables low-resolution images to reconstruct high-resolution images and aids in the early diagnosis of medical CT images. The network mainly consists of a low-level feature extraction module, a cyclic feature concentration block, and a reconstruction module. First, the underlying feature extraction module extracts the effective feature information of the low-resolution image. Then, the cyclic feature concentration block further extracts the feature information. Finally, the reconstruction module reconstructs the high-definition image. It can be seen from the experiment results that the method proposed in this paper achieved the best results on two objective indicators.

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Acknowledgment

This work is supported in part by grants from the China Postdoctoral Science Foundation No. 2017M611905, Suzhou Industrial Technology Innovation Special Project (Liveli-hood Technology)—Technology Demonstration Project No. SS201701 No. SYSD2019152, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Xingchen Liu & Juncheng Jia

Authors
  1. Xingchen Liu
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  2. Juncheng Jia
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Corresponding author

Correspondence to Juncheng Jia .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Cite this paper

Liu, X., Jia, J. (2020). Medical CT Image Super-Resolution via Cyclic Feature Concentration Network. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-60633-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60632-9

  • Online ISBN: 978-3-030-60633-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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