This thesis develops a strategy for polymerized volume data set construction.
Given a volume data set defined over a regular three-dimensional grid, a polymerized
volume data set (PVDS) can be defined as follows: edges between adjacent vertices of
the grid are labeled 1 (active) or 0 (inactive) to indicate the likelihood that an edge is
contained in (or spans the boundary of) a common underlying object, adding information
not in the original volume data set. This edge labeling Âpolymerizes adjacent voxels
(those sharing a common active edge) into connected components, facilitating
segmentation of embedded objects in the volume data set. Polymerization of the volume
data set also aids real-time data compression, geometric modeling of the embedded
objects, and their visualization.
To construct a polymerized volume data set, an adjacency class within the grid
system is selected. Edges belonging to this adjacency class are labeled as interior,
exterior, or boundary edges using discriminant functions whose functional forms are
derived for three local adjacency classes. The discriminant function parameter values are
determined by supervised learning. Training sets are derived from an initial
segmentation on a homogeneous sample of the volume data set, using an existing
segmentation method.
The strategy of constructing polymerized volume data sets is initially tested on
synthetic data sets which resemble neuronal volume data obtained by three-dimensional
microscopy. The strategy is then illustrated on volume data sets of mouse brain
microstructure at a neuronal level of detail. Visualization and validation of the resulting
PVDS is shown in both cases. Finally the procedures of polymerized volume data set construction are
generalized to apply to any Bravais lattice over the regular 3D orthogonal grid. Further
development of this latter topic is left to future work.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3256 |
| Date | 12 April 2006 |
| Creators | Aragonda, Prathyusha |
| Contributors | McCormick, Bruce H. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 2896394 bytes, electronic, application/pdf, born digital |
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