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

  • Conference paper
  • First Online:
Information Processing in Medical Imaging (IPMI 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 687))

Abstract

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 information

Authors and Affiliations

  1. Image Science, ETH-Zentrum, Communication Technology Laboratory, CH-8092, Zurich, Switzerland

    G. Gerig, Th. Koller, G. Székely, Ch. Brechbühler & O. Kübler

Authors
  1. G. Gerig
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  2. Th. Koller
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  3. G. Székely
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  4. Ch. Brechbühler
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  5. O. Kübler
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Editor information

Harrison H. Barrett A. F. Gmitro

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© 1993 Springer-Verlag Berlin Heidelberg

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Gerig, G., Koller, T., Székely, G., Brechbühler, C., Kübler, O. (1993). Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. In: Barrett, H.H., Gmitro, A.F. (eds) Information Processing in Medical Imaging. IPMI 1993. Lecture Notes in Computer Science, vol 687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013783

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  • DOI: https://doi.org/10.1007/BFb0013783

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56800-1

  • Online ISBN: 978-3-540-47742-6

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