The scientific aim of computational neuroanatomy using magnetic resonance imaging (MRI) is to quantify inter- and intra-subject morphological variabilities. A unified statistical framework for analyzing temporally varying brain morphology is presented. Based on the mathematical framework of differential geometry, the deformation of the brain is modeled and key morphological descriptors such as length, area, volume dilatation and curvature change are computed. To increase the signal-to-noise ratio, Gaussian kernel smoothing is applied to 3D images. For 2D curved cortical surface, diffusion smoothing, which generalizes Gaussian kernel smoothing, has been developed. Afterwards, statistical inference is based on the excursion probability of random fields defined on manifolds. / This method has been applied in localizing the regions of brain tissue growth and loss in a group of 28 normal children and adolescents. It is shown that children's brains change dramatically in localized areas even after age 12.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.37880 |
| Date | January 2001 |
| Creators | Chung, Moo K., 1969- |
| Contributors | Worsley, K. J. (advisor), Ramsay, J. O. (advisor) |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Mathematics and Statistics.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001846517, proquestno: NQ75619, Theses scanned by UMI/ProQuest. |
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