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    Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer

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    Name:
    AIIm_revised_final.pdf
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    1.248Mb
    Format:
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    Description:
    Final Accepted Manuscript
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    Author
    Niazi, M Khalid Khan
    Lin, Y
    Liu, F
    Ashok, A
    Marcellin, M W
    Tozbikian, G
    Gurcan, M N
    Bilgin, A
    Affiliation
    Univ Arizona
    Issue Date
    2019-04-01
    Keywords
    Alpha shapes
    Compression
    Hotspot detection
    JPIP
    Ki-67
    Pathology images
    
    Metadata
    Show full item record
    Publisher
    ELSEVIER SCIENCE BV
    Citation
    Niazi, M. K. K., Lin, Y., Liu, F., Ashok, A., Marcellin, M. W., Tozbikian, G., ... & Bilgin, A. (2019). Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer. Artificial intelligence in medicine, 95, 82-87.
    Journal
    ARTIFICIAL INTELLIGENCE IN MEDICINE
    Rights
    © 2018 Elsevier B.V. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements.
    Note
    12 month embargo; published online: 25 September 2018
    ISSN
    1873-2860
    PubMed ID
    30266546
    DOI
    10.1016/j.artmed.2018.09.002
    Version
    Final accepted manuscript
    Sponsors
    National Institutes of Health [NCI 1U01CA198945-01]
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0933365717302324?via%3Dihub
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.artmed.2018.09.002
    Scopus Count
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    UA Faculty Publications

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