IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3652-d934290.html
   My bibliography  Save this article

Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images

Author

Listed:
  • Josep Arnal

    (Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Campus de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain
    These authors contributed equally to this work.)

  • Luis Súcar

    (Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Campus de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain
    These authors contributed equally to this work.)

Abstract

To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.

Suggested Citation

  • Josep Arnal & Luis Súcar, 2022. "Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3652-:d:934290
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3652/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3652/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming-Hao Lin & Zhi-Xiang Hou & Kai-Han Cheng & Chin-Hsien Wu & Yan-Tsung Peng, 2021. "Image Denoising Using Adaptive and Overlapped Average Filtering and Mixed-Pooling Attention Refinement Networks," Mathematics, MDPI, vol. 9(10), pages 1-12, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3652-:d:934290. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.