CT-Assisted Engineering and Manufacturing

  • Jolyon A. Browne
  • Mathew Koshy
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


X-ray computed tomography (CT) is an important and powerful tool in industrial imaging for obtaining shape and dimensional information of industrial parts. It also serves to provide digital models of parts for inputs to new and emerging technologies in the manufacturing industry that have begun to embrace CT-assisted engineering and design. Since a large number of objects encountered in industrial CT are made either of a single homogenous material or a few homogenous materials, algorithms for discrete tomography should, in principle, yield CT images whose resolution and dimensional accuracy are superior to CT images obtained by conventional algorithms. This in turn should result in significant improvements in the accuracy of boundaries extracted from CT images for the creation of digital models of a large class of parts of interest in CT-assisted manufacturing. This chapter looks at some important applications in CT-assisted engineering and manufacturing that can benefit from the techniques of discrete tomography, and discuss some of the technical challenges faced in extracting boundaries with the degree of accuracy demanded for engineering and manufacturing applications.


Point Cloud Reverse Engineering Digital Model Discrete Tomography Agile Manufacturing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    G. T. Herman, Image Reconstruction from Projections: The Fundamentals of Computerized Tomography (Academic Press, New York), 1980.Google Scholar
  2. [2]
    A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Press, New York), 1988.Google Scholar
  3. [3]
    J. L. C. Sanz, E. B. Hinkle, and A. K. Jain, Radon and Projection Transform-Based Computer Vision: Algorithms, A Pipeline Architecture, and Industrial Applications (Springer Verlag, Berlin), 1988.Google Scholar
  4. [4]
    M. C. San Martin, N. P. J. Stamford, N. Dammerova, N. Dixon, and J. M. Carazo, “A structural model of the DnaB helicase from E. Coli based on three-dimensional electron microscopy data,” J. Structural Biology 114, 167–176 (1995). CrossRefGoogle Scholar
  5. [5]
    P. Schwander, C. Kisielowski, M. Seibt, F. H. Baumann, Y. Kim, and A. Ourmazd, “Mapping projected potential, interfacial roughness and chemical composition in general crystalline solids by quantitative transmission electron microscopy,” Phys. Rev. Lett. 71, 4150–4153 (1993).CrossRefPubMedGoogle Scholar
  6. [6]
    R. A. Brualdi and H. J. Ryser, Combinatorial Matrix Theory (Cambridge University Press, New York), 1991.CrossRefGoogle Scholar
  7. [7]
    N. J. Dusaussoy, Q. Cao, R. N. Yancey, and J. H. Stanley, “Image processing for CT-assisted reverse engineering and part characterization,” Proc. 1995 IEEE Intl. Conf. on Image Processing Vol. 3, (IEEE Computer Society Press, California), pp. 33–36, 1995.Google Scholar
  8. [8]
    R. N. Yancey, “Analysis of Stress Distributions in Metal-Matrix Composites with Variations in Fiber Spacing,” Ph.D. Dissertation, University of Dayton, Dayton, Ohio (1997).Google Scholar
  9. [9]
    A. Rosenfeld and A. C. Kak, Digital Picture Processing, Vol. 2 (Academic Press, New York), 1982.Google Scholar
  10. [10]
    T. Y. Kong and A. Rosenfeld, “Digital topology: Introduction and survey,” Comp. Vis., Graphics,Image Process. 48 357–393 (1989).CrossRefGoogle Scholar
  11. [11]
    J. K. Udupa, “Multidimensional digital boundaries,” CVGIP: Graphical Models Image Process. 56 311–323 (1994).Google Scholar
  12. [12]
    A. Kuba and A. Volcic, “Characterisation of measurable plane sets which are reconstructible from their two projections,” Inverse Problems 4, 513–527 (1988).CrossRefGoogle Scholar
  13. [13]
    A. Shilferstein and Y. T. Chen, “Switching components and the ambiguity problem in the reconstruction of pictures from their projections,” Pattern Recogn. 10 327–340 (1978).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Jolyon A. Browne
    • 1
  • Mathew Koshy
    • 2
  1. 1.Advanced Research and Applications Corporation (ARACOR)SunnyvaleUSA
  2. 2.Advanced Research and Applications Corporation (ARACOR)SunnyvaleUSA

Personalised recommendations