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

G. Gerig; Th Koller; G. Székely; Ch Brechbühler; O. Kübler

doi:10.1007/bfb0013783

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The present paper focuses on the conversion of multidimensional image structures to art 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 symbofic description which represents topological and geometrical features of tree-fike, 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-fines while fully preserving the 3-D topology. The local width of the fine structures is characterized by a separate 3-D Eucfidean distance transform. Compilation, or raster-to-vector transformation, converts the maximally thinned voxel fists into a vector description. The final graph data-structure encodes the spatial course of fine sections, the estimate of the local diameter, and the topology at important key locations fike branchings and end-points. The analysis system is appfied to the characterization of the cerebral vascular system segmented from magnetic resonance angiography (MRA).

Original language	English (US)
Title of host publication	Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings
Editors	Harrison H. Barrett, Arthur F. Gmitro
Publisher	Springer Verlag
Pages	94-111
Number of pages	18
ISBN (Print)	9783540568001
DOIs	https://doi.org/10.1007/bfb0013783
State	Published - 1993
Event	13th International Conference on Information Processing in Medical Imaging, IPMI 1993 - Flagstaff, United States Duration: Jun 14 1993 → Jun 18 1993

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	687 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th International Conference on Information Processing in Medical Imaging, IPMI 1993
Country	United States
City	Flagstaff
Period	6/14/93 → 6/18/93

Keywords

3-D Binary thinning
3-D Raster-tovector transform
Cerebrospinal vasculature
Magnetic resonance angiography
Multidimensional image analysis
Symbofic representation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/bfb0013783

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Cite this

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 H. H. Barrett, & A. F. Gmitro (Eds.), Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings (pp. 94-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 687 LNCS). Springer Verlag. https://doi.org/10.1007/bfb0013783

Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. / Gerig, G.; Koller, Th; Székely, G.; Brechbühler, Ch; Kübler, O.

Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings. ed. / Harrison H. Barrett; Arthur F. Gmitro. Springer Verlag, 1993. p. 94-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 687 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 HH Barrett & AF Gmitro (eds), Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 687 LNCS, Springer Verlag, pp. 94-111, 13th International Conference on Information Processing in Medical Imaging, IPMI 1993, Flagstaff, United States, 6/14/93. https://doi.org/10.1007/bfb0013783

Gerig G, Koller T, Székely G, Brechbühler C, Kübler O. Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. In Barrett HH, Gmitro AF, editors, Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings. Springer Verlag. 1993. p. 94-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/bfb0013783

Gerig, G. ; Koller, Th ; Székely, G. ; Brechbühler, Ch ; Kübler, O. / Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data. Information Processing in Medical Imaging - 13th International Conference, IPMI 1993, Proceedings. editor / Harrison H. Barrett ; Arthur F. Gmitro. Springer Verlag, 1993. pp. 94-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The present paper focuses on the conversion of multidimensional image structures to art 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 symbofic description which represents topological and geometrical features of tree-fike, 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-fines while fully preserving the 3-D topology. The local width of the fine structures is characterized by a separate 3-D Eucfidean distance transform. Compilation, or raster-to-vector transformation, converts the maximally thinned voxel fists into a vector description. The final graph data-structure encodes the spatial course of fine sections, the estimate of the local diameter, and the topology at important key locations fike branchings and end-points. The analysis system is appfied to the characterization of the cerebral vascular system segmented from magnetic resonance angiography (MRA).",

keywords = "3-D Binary thinning, 3-D Raster-tovector transform, Cerebrospinal vasculature, Magnetic resonance angiography, Multidimensional image analysis, Symbofic representation",

author = "G. Gerig and Th Koller and G. Sz{\'e}kely and Ch Brechb{\"u}hler and O. K{\"u}bler",

note = "Funding Information: Part of this research work was funded by COVIRA (Computer Vision in Radiology), project A2003 of the AIM (Advanced Informatics in Medicine) programme of the European Commission. Participants in the COVIRA consortium are: Philips Medical Systems, Best (NL) and Madrid (E), Corporate Research, Hamburg (D) (prime contractor); Siemens AG, Erlangen (D) and Munich (D); IBM US Scientific Centre, Winchester (UK); Gregorio Maranon General Hospital, Madrid (E); University of Tfibingen, Neuroradiology and Theoretical Astrophysics (D); German Cancer Research Centre, Heidelberg (D); University of Leuven, Neurosurgery, Radiology and Electrical Engineering (B); University of Utrecht, Neurosurgery and Computer Vision (NL); Royal Marsden Hospital/Institute of Cancer Research, Sutton (UK); National Hospital for Neurology and Neurosurgery, London (UK); Foundation of Research and Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1993. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 13th International Conference on Information Processing in Medical Imaging, IPMI 1993 ; Conference date: 14-06-1993 Through 18-06-1993",

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N2 - The present paper focuses on the conversion of multidimensional image structures to art 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 symbofic description which represents topological and geometrical features of tree-fike, 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-fines while fully preserving the 3-D topology. The local width of the fine structures is characterized by a separate 3-D Eucfidean distance transform. Compilation, or raster-to-vector transformation, converts the maximally thinned voxel fists into a vector description. The final graph data-structure encodes the spatial course of fine sections, the estimate of the local diameter, and the topology at important key locations fike branchings and end-points. The analysis system is appfied to the characterization of the cerebral vascular system segmented from magnetic resonance angiography (MRA).

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KW - 3-D Raster-tovector transform

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