Publication: Type-specific analysis of morphometry of dendrite spines of mice
dc.contributor.coauthor | Fong L. | |
dc.contributor.coauthor | Tasky T.N. | |
dc.contributor.coauthor | Hurdal M.K. | |
dc.contributor.coauthor | Beg M.F. | |
dc.contributor.coauthor | Martone M.E. | |
dc.contributor.coauthor | Ratnanather J.T. | |
dc.contributor.department | Department of Mathematics | |
dc.contributor.kuauthor | Ceyhan, Elvan | |
dc.contributor.schoolcollegeinstitute | College of Sciences | |
dc.date.accessioned | 2024-11-09T23:18:32Z | |
dc.date.issued | 2007 | |
dc.description.abstract | In this article, we analyze the morphometric measures of dendrite spines of mice derived from electron tomography images for different spine types based on pre-assigned categories. The morphometric measures we consider include the metric distance, volume, surface area, and length of dendrite spines of mice. The question of interest is how these morphometric measures differ by condition of mice; and how the metric distance relates to volume, surface area, length, and condition of mice. The Large Deformation Diffeomorphic Metric Mapping algorithm is the tool we use to obtain the metric distances that quantize the morphometry of binary images of dendrite spines with respect to a template spine. We demonstrate that for the values not adjusted for scale metric distances and other morphometric measures are significantly different between the conditions. The morphometric measures (rather than the mice condition) explain almost all the variation in metric distances. Since size (or scale) dominates the other variables in variation, we adjust metric distances and other morphometric measures for scale. We demonstrate that the scaled metric distances and other scaled morphometric variables still differ for condition, and scaled metric distances depend most significantly on scaled morphometric measures. The methodology used is also valid for morphometric measures of other organs or tissues and metric distances other than LDDMM. | |
dc.description.indexedby | Scopus | |
dc.description.openaccess | YES | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | IEEE Region 8 | |
dc.description.sponsorship | IEEE Croatia Section - CAS and SP Society Chapter | |
dc.description.sponsorship | IEEE Croatia Section - Computer Society Chapter | |
dc.identifier.doi | 10.1109/ISPA.2007.4383655 | |
dc.identifier.isbn | 9789-5318-4116-0 | |
dc.identifier.quartile | N/A | |
dc.identifier.scopus | 2-s2.0-47949114427 | |
dc.identifier.uri | https://doi.org/10.1109/ISPA.2007.4383655 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/10392 | |
dc.keywords | Conformal mapping | |
dc.keywords | Dendrites (metallography) | |
dc.keywords | Diagnostic radiography | |
dc.keywords | Electric impedance tomography | |
dc.keywords | Image enhancement | |
dc.keywords | Medical imaging | |
dc.keywords | Signal processing | |
dc.keywords | Tomography | |
dc.keywords | Electron tomographies | |
dc.keywords | Mapping algorithms | |
dc.keywords | Metric distances | |
dc.keywords | Morphometric variables | |
dc.keywords | Morphometry | |
dc.keywords | Surface areas | |
dc.keywords | Theorem proving | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | ISPA 2007 - Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis | |
dc.subject | Mathematics | |
dc.title | Type-specific analysis of morphometry of dendrite spines of mice | |
dc.type | Conference Proceeding | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Ceyhan, Elvan | |
local.publication.orgunit1 | College of Sciences | |
local.publication.orgunit2 | Department of Mathematics | |
relation.isOrgUnitOfPublication | 2159b841-6c2d-4f54-b1d4-b6ba86edfdbe | |
relation.isOrgUnitOfPublication.latestForDiscovery | 2159b841-6c2d-4f54-b1d4-b6ba86edfdbe | |
relation.isParentOrgUnitOfPublication | af0395b0-7219-4165-a909-7016fa30932d | |
relation.isParentOrgUnitOfPublication.latestForDiscovery | af0395b0-7219-4165-a909-7016fa30932d |