TY - GEN
T1 - Analysis of MR angiography volume data leading to the structural description of the cerebral vessel tree
AU - Székely, G.
AU - Gerig, G.
AU - Koller, Th
AU - Brechbühler, Ch
AU - Kübler, O.
PY - 1993
Y1 - 1993
N2 - The performance of computer assisted systems for presentation, manipulation and quantitation of objects obtained from multidimensional image data depends critically on the ability to segment and describe structures in images. We describe the development of a prototype system that extracts three-dimensional (3-D) curvilinear structures from volume image data and converts them into a symbolic description which is more appropriate to assess features of tree-like, filamentous objects. The initial segmentation is performed by 3-D line filtering and/or 3-D hysteresis thresholding. A skeletal structure is derived by 3-D binary thinning, approximating the center-line by pseudo-parallel erosion while fully preserving the 3-D topology. The final graph datastructure 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 system is applied to analyze the cerebral vascular system resulting from magnetic resonance angiography (MRA).
AB - The performance of computer assisted systems for presentation, manipulation and quantitation of objects obtained from multidimensional image data depends critically on the ability to segment and describe structures in images. We describe the development of a prototype system that extracts three-dimensional (3-D) curvilinear structures from volume image data and converts them into a symbolic description which is more appropriate to assess features of tree-like, filamentous objects. The initial segmentation is performed by 3-D line filtering and/or 3-D hysteresis thresholding. A skeletal structure is derived by 3-D binary thinning, approximating the center-line by pseudo-parallel erosion while fully preserving the 3-D topology. The final graph datastructure 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 system is applied to analyze the cerebral vascular system resulting from magnetic resonance angiography (MRA).
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U2 - 10.1007/3-540-57233-3_93
DO - 10.1007/3-540-57233-3_93
M3 - Conference contribution
AN - SCOPUS:33747511734
SN - 9783540572336
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 687
EP - 692
BT - Computer Analysis of Images and Patterns - 5th International Conference, CAIP 1993, Proceedings
A2 - Chetverikov, Dmitry
A2 - Kropatsch, Walter G.
PB - Springer Verlag
T2 - 5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993
Y2 - 13 September 1993 through 15 September 1993
ER -