Abstract
In several medical applications it is necessary to have a good reconstruction of approximately tubular structures — mainly blood vessels but also intestine or bones — providing a description of both the internal lumen (usually a triangulated surface) and its networked structure (skeleton). This description should be such that it allows lengths and diameters estimation. Several methods have been proposed for these tasks, each one with advantages and drawbacks and, typically, specialized to a particular application. We focused our attention on methods making as few assumptions as possible on the structure to be determined in order to capture also anomalous features like bulges and bifurcations. We looked for a method able to obtain surfaces that are smooth, with a limited number of triangles but accurate and skeletons that are continuously connected and centered. The results of our work is the use of customized deformable surface and multi-scale regularized voxel coding centerlines to obtain geometries and skeletons with the desired properties. The algorithms are being tested for real clinical analysis and results are promising.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AQUATICS project web site: http://aquatics.crs4.it.
G. Abdulaev et. al., “ViVa: The Virtual Vascular Project” IEEE trans. on Information Technology in medicine, 14:1 34–48 (1998)
G. Bertrand and G. Malandain, “A new characterization of three dimensional simple points” Pattern Rec.Lett, 15,2 169–175 (1994)
D. Attali and A. Montanvert, “Computing and Simplifying 2D and 3D Semicontinuous Skeletons of 2D and 3D shapes” CVIU, Vol. 67, No. 3, September, pages 261–273, (1997)
T. K. Dey and W. Zhao, “Approximate medial axis as a Voronoi subcomplex” Proc. 7th ACM Sympos. Solid Modeling Applications, 356–366 (2002)
A. Verroust and F. Lazarus, “Extracting Skeletal Curves from 3D Scattered Data” In The Visual Computer. Vol 16.No 1, pp 15–25. 2000.
S.R. Aylward, “Initialization, Noise, Singularities and Scale in Height Ridge Traversal for Tubular Object Centerline Extraction” IEEE Trans. on Medical Imaging, 21,2 61–75 (2002)
L. Florez-Valencia, J. Montagnat, M. Orkisz, “3D Graphical models for vascularstent pose simulation” proc. ICCVG 2002 Zakopane
A.F. Frangi et, al., “Quantitative Analysis of Vascular Morphology from 3D MR angiograms: in Vitro and In Vivo Results” Magn Res. in Med. 45, 311–322 (2001)
F.K.H. Quek and C. Kirbas, “Vessel extraction in Medical Images by Wave Propagation and Traceback” IEEE Trans. on Medical Imaging, 2,20 pp. 117–131 (2002)
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3D surface construction algorithm, ” Proc. ACM Conference on Computer Graphics (SIGGRAPH’87), Anaheim, USA, pp. 163–169, July 1987.
T. Mc Inrey and D. Terzopulos, “Deformable models in medical images analysis:a survey” Medical Image Analysis, 1(2):91–108, 1996.
H. Delingette, “Simplex meshes: a general representation for 3d shape reconstruction”, in CVPR94, pp. 856–859, 1994.
K. Krissian et al. “Model Based Multiscale Detection and Reconstruction of 3D vessels” INRIA Sophie Antipolis Report n.3442 (1998)
Montagnat, J, Delingette, H, and Ayache, N, “A review of deformable surfaces: topology, geometry and deformation,” Image and Vision Computing, vol. 19, pp. 1023–1040, 2001.
Yong Zhou, Arthur W. Toga, Efficient Skeletonization of Volumetric Objects IEEE Transactions on Visualization and Computer Graphics 5:3 July–September 1999
M. Wan, F. Dachille, and A. Kaufman (2001) “Distance-Field Based Skeletons for Virtual Navigation, ” Visualization 2001, San Diego, CA, October 2001
D. Chen et al, “A Novel Approach to Extract Colon Lumen from CT Images for Virtual Colonoscopy,” IEEE Transactions on Medical Imaging, Vol. 19, No. 12, December 2000, pp. 1220–1226.
I. Bitter, A. Kaufman and M. Sato(2001) “Penalized-Distance Volumetric Skeleton Algorithm,” IEEE Transactions on Visualization and Computer Graphics, Vol. 7, No. 3, July–Sept. 2001, pp. 195–206
R.J.T. Sadleir and P.F. Whelan, “Colon Centerline Calculation for CT Colonography using Optimised 3D topological thinning” Proc. IEEE 3DPVT, pp.800–803 (2002)
H. Blum, “A tranformation for extracting new descriptors of shape, “Proc. Symp. Models for the Perception of Speech and Visual Form (W.W. Dunn, Ed.), MIT Press, Cambridge, MA, pp. 362–380, 1967
A. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active contour models,” Int. J. of Comp. Vision 1, 321–331 (1988).
H. Bourquain et al., “HepaVision2-a software assistant for preoperative planning in LRLT and oncologic liver surgery”, Computer Assisted Radiology and Surgery (CARS), June 2002, pp. 341–346
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giachetti, A., Zanetti, G. (2003). 3D Reconstruction of Large Tubular Geometries from CT Data. In: Ayache, N., Delingette, H. (eds) Surgery Simulation and Soft Tissue Modeling. IS4TM 2003. Lecture Notes in Computer Science, vol 2673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45015-7_13
Download citation
DOI: https://doi.org/10.1007/3-540-45015-7_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40439-2
Online ISBN: 978-3-540-45015-3
eBook Packages: Springer Book Archive