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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorDrew, Donald A. (Donald Allen), 1945-
dc.contributorLigon, Lee
dc.contributorMcLaughlin, H. W.
dc.contributorMitchell, John E.
dc.contributor.authorDiLorenzo, Tyson A.
dc.date.accessioned2021-11-03T08:28:30Z
dc.date.available2021-11-03T08:28:30Z
dc.date.created2015-10-01T11:34:31Z
dc.date.issued2015-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1538
dc.descriptionAugust 2015
dc.descriptionSchool of Science
dc.description.abstractMicrotubules 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.
dc.description.abstractThe 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.
dc.description.abstractImages 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectMathematics
dc.titleClassifying microtubule networks using curvature calculation of discrete curves
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid176750
dc.digitool.pid176751
dc.digitool.pid176752
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Mathematical Sciences


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