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Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data

G. Gerig, T. Koller, G. Székely, C. Brechbühler and O. Kübler
Information Processing in Medical Imaging, Proc. IPMI'93


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).

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  author = {G. Gerig and T. Koller and G. Székely and C. Brechb\"uhler and O. K\"ubler},
  title = {Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data},
  booktitle = {Information Processing in Medical Imaging, Proc. IPMI'93},
  year = {1993},
  pages = {94--111},
  editor = {H.H. Barrett and A.F. Gmitro},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer Verlag},
  keywords = {medical image analysis, model-based, thinning, morphometry, simulation, brain, understanding}