GPU-Based Texture Analysis approach for Mammograms Institute of Biomedical Informatics

  • Che Lun Hung
  • , Chun Yuan Lin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations

    Abstract

    Mammograms are always used to detect signs of breast cancer. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps, and they can be utilized to retravel specific features of symptoms from medical images produced by X-ray, magnetic resonance imaging, computed tomography, and so forth. Gray level run-length matrix is the one of the texture extraction methods which has been successfully used to facilitate medical image analysis. However, it is computation-intensive method. We implemented it on GPU to accelerating extraction process for mammograms. The proposed method achieves significant speedup over CPU-based implementation.

    Original languageEnglish
    Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
    EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2183-2186
    Number of pages4
    ISBN (Electronic)9781728162157
    DOIs
    StatePublished - 16 12 2020
    Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
    Duration: 16 12 202019 12 2020

    Publication series

    NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

    Conference

    Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
    Country/TerritoryKorea, Republic of
    CityVirtual, Seoul
    Period16/12/2019/12/20

    Bibliographical note

    Publisher Copyright:
    © 2020 IEEE.

    Keywords

    • Feature Extraction
    • GLRLM
    • GPU
    • mammogram

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