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    3-D Neuroimaging Segmentation

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    Bhargava_umbc_0434M_12376.pdf (2.372Mb)
    Permanent Link
    http://hdl.handle.net/11603/24173
    Collections
    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Graduate School
    • UMBC Student Collection
    • UMBC Theses and Dissertations
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    Author/Creator
    Bhargava, Bhrigu
    Date
    2020-01-01
    Type of Work
    application:pdf
    Text
    theses
    Department
    Computer Science and Electrical Engineering
    Program
    Computer Science
    Rights
    Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
    Abstract
    We present automated anatomical segmentation of 3D neuroimaging of head Magnetic Resonance Imaging (MRI) scans. Delineation of the anatomical structures of neuroimaging data can play an important role in measuring, analyzing, and visualizing the brain's anatomical structure, changes, tumor detection, assist in surgical planning, and cognitive neuroscience research. We process MRI scans for intensity-based anatomical segmentation by performing multilevel preprocessing steps (brain extraction, bias field reduction, and partial volume effect removal). We enhance the boundary guidelines by using gradient magnitude and then reproduce the state of art 2-D efficient graph-based image segmentation technique to work on 3-D images and produce guided segmentation in O (n log n) time complexity. We test our algorithm's performance in different applications like brain anatomical segmentation, brain tumor detection, hippocampus segmentation, and MRI sequencing techniques comparison. Dice coefficient and Jaccard's coefficient are used to validate segmentation results with ground truth and individual segmentation.


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
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    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.

     

     

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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.