Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data
The present paper focuses on the conversion of multidimensional image structures to an object-centered, abstract description encoding shape features and structure relationships. We describe a prototype system that extracts three-dimensional (3-D) curvilinear structures from volume image data and transforms them into a symbolic description which represents topological and geometrical features of tree-like, filamentous objects.
The initial segmentation is performed by 3-D hysteresis thresholding. A skeletal structure is derived by 3-D binary thinning, approximating the center-lines while fully preserving the 3-D topology. The local width of the line structures is characterized by a separate 3-D Euclidean distance transform. Compilation, or raster-to-vector transformation, converts the maximally thinned voxel lists into a vector description. The final graph data-structure encodes the spatial course of line sections, the estimate of the local diameter, and the topology at important key locations like branchings and end-points.
The analysis system is applied to the characterization of the cerebral vascular system segmented from magnetic resonance angiography (MRA).
KeywordsMultidimensional image analysis 3-D binary thinning 3-D raster-tovector transform symbolic representation magnetic resonance angiography cerebrospinal vasculature
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