Advertisement

A new model for the recovery of cylindrical structures from medical image data

  • Thomas O'Donnell
  • Alok Gupta
  • Terrance Boult
Analysis of Cardiac and Vascular Images
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1205)

Abstract

We introduce a novel analytic model formulation for recovering cylindrical structures (e.g., blood vessels) from segmented 3-D medical image data. Unlike all previous formulations, our model is capable of describing a cylinder with an arbitrary spine (a space curve based on cubic B-splines) and arbitrary cross section which is guaranteed to be orthogonal to the spine. Given this expressiveness, we are able to provide a second order continuous approximation to the centerline of nearly any tubular object. This information may be used for such tasks as a reformatting of the original image data in order to visually detect stenoses or aneurysms. In addition, the cross-section parameter values of our model may aid in automatically isolating these regions. We maintain a relatively simple cross-section function to make this detection straightforward (note that any cross-section function is possible). To describe fine detail in the data, we employ local finite element deformations from the model surface. Thus we are able to recover gross geometric approximations as well as quantify characteristics of the object such as its surface area. We apply our model to the recovery of both a healthy and diseased aorta from segmented CT acquisitions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    F. Klok. Two moving coordinate frames for sweeping along a 3d trajectory. Computer-Aided Geometric Design, 3(3):217–229, November 1986.CrossRefGoogle Scholar
  2. 2.
    A. Gupta, L von Kurowski, A. Singh, D. Geiger, C-C. Liang, M-Y. Chiu, L. P. Adler, M. Haacke, and D. L. Wilson. Cardiac mr image segmentation using deformable models. In Proceedings of Conference on Computers in Cardiology, pages 747–750. IEEE, 1993.Google Scholar
  3. 3.
    Q. Huang and G. C. Stockman. Generalized tube model: Recognizing 3d elongated objects from 2d intensity images. In Proceedings of CVPR., pages 104–109. IEEE, 1993.Google Scholar
  4. 4.
    Thomas O'Donnell, Xi-Sheng Fang, Alok Gupta, and Terrance Boult. The extruded generalized cylinder: A deformable model for object recovery. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, June 1994.Google Scholar
  5. 5.
    J. Sequiera, R. Ebel, and F. Scmitt. Three-dimensional modeling of tree-like anatomical structures. Computerized Medical Imaging and Graphics, 17:333–337, 1993CrossRefPubMedGoogle Scholar
  6. 6.
    U. Shani and D. Ballard. Splines as embeddings for generalized cylinders. CVGIP: Image Understanding, 27:129–156, 1984.Google Scholar
  7. 7.
    D. Terzopoulos and D. Metaxas. Dynamic 3D models with local and global deformations: Deformable superquadrics. IEEE PAMI, 13(7):703–714, 1991.Google Scholar
  8. 8.
    D. Terzopoulos, A. Witkin, and M. Kass. Constraints on deformable models: Recovering 3d shape and nonrigid motion. Artificial Intelligence, pages 91–123, August 1988.Google Scholar
  9. 9.
    Mourad Zerroug and Ramakant Nevatia. Quasi-invariant properties and 3-d shape recovery of non-straight, non-constant generalized cylinders. In Proceedings of the CVPR, pages 96–103. IEEE, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Thomas O'Donnell
    • 1
  • Alok Gupta
    • 1
  • Terrance Boult
    • 2
  1. 1.Siemens Corp Research, Inc.East Princeton
  2. 2.Dept. of Computer ScienceLehigh UniversityBethlehem

Personalised recommendations