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    Classifying microtubule networks using curvature calculation of discrete curves

    Author
    DiLorenzo, Tyson A.
    View/Open
    176751_DiLorenzo_rpi_0185E_10739.pdf (5.353Mb)
    Other Contributors
    Drew, Donald A. (Donald Allen), 1945-; Ligon, Lee; McLaughlin, H. W.; Mitchell, John E.;
    Date Issued
    2015-08
    Subject
    Mathematics
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/1538
    Abstract
    Microtubules are structures within the cell that form a transportation network along which motor proteins tow cargo to destinations within the cell. To establish and maintain a structure capable of serving the cell's tasks, microtubules undergo deconstruction and reconstruction regularly. This change in structure is critical to tasks like wound repair and cell motility.; The method is employed on images of subpopulations of microtubules in cells to test if the subpopulations can be classified from the total population by angle distribution, curvature distribution, mean of curvature, and variance of curvature.; Images of fluorescing microtubule networks are captured in grayscale at different wavelengths, displaying different tagged proteins. The analysis of these polymeric structures involves identifying the presence of the protein and the direction of the structure in which it resides. The method presented finds statistical properties of parts of microtubules. The method processes the captured image by finding a microtubule point in the image, and determining a direction at that point. The method is then refined to estimate angular direction and curvature of the microtubules, statistically estimate the direction of microtubules in a region of the cell, and compare properties of different types of microtubule networks in the same region. To verify accuracy, we study results of the method on a test image.;
    Description
    August 2015; School of Science
    Department
    Dept. of Mathematical Sciences;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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