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Combining Thickness Information with Surface Tensor-based Morphometry for the 3D Statistical Analysis of the Corpus Callosum


Abstract In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to comp... (more)
Created Date 2013
Contributor Xu, Liang (Author) / Wang, Yalin (Advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Subject Computer science / Bioinformatics / Biostatistics / Blindness / Corpus Callosum / Laplace's equation / MRI / Tensor-based morphometry / Thickness
Type Masters Thesis
Extent 44 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis