Skip to main content

On reconstruction and segmentation of piecewise continuous images

  • 2. Incorporation Of Priors In Tomographic Reconstraction
  • Conference paper
  • First Online:
Information Processing in Medical Imaging (IPMI 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 511))

Abstract

We evaluate an image model for simultaneous reconstruction and segmentation of piecewise continuous images. The model assumes that the intensities of the piecewise continuous image are relatively constant within contiguous regions and that the intensity levels of these regions can be determined either empirically or theoretically before reconstruction. The assumptions might be valid, for example, in cardiac blood-pool imaging or in transmission tomography of the thorax for non-uniform attenuation correction of emission tomography. In the former imaging situation, the intensities or radionuclide activities within the regions of myocardium, blood-pool and background may be relatively constant and the three activity levels can be distinct. For the latter case, the attenuation coefficients of bone, lungs and soft tissues can be determined prior to reconstructing the attenuation map. The contiguous image regions are expected to be simultaneously segmented during image reconstruction. We tested the image model with experimental phantom studies. The phantom consisted of a plastic cylinder having an elliptical cross section and containing five contiguous regions. There were three distinct activity levels within the phantom. Projection data were acquired using a SPECT system. Reconstructions were performed using an iterative maximum a posteriori probability procedure. As expected, the reconstructed image consisted of contiguous regions and the acitivities within the regions were relatively constant. Compared with maximum likelihood and a Bayesian approach using a Gibbs prior, the results obtained using the image model demonstrated the improvement in identifying the contiguous regions and the associated activities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Akaike H (1974). A New Look at the Statistical Model Identification. IEEE Trans. Auto. Control, vol.19, pp.716–723.

    Google Scholar 

  • Akaike H (1978). A Bayesian Analysis of the Minimum AIC Procedure. Ann. Inst. Statist. Math., vol.30, pp.9–14.

    Google Scholar 

  • Baxter L (1990). Futures of Statistics. The Amer. Statistician, vol.44, pp.128–129.

    Google Scholar 

  • Besag J (1974). Spatial Interation and the Statistical Analysis of Lattice Systems. J.R. Stat. Soc., series B, vol.26, pp.192–236.

    Google Scholar 

  • Budinger T and Gullberg G (1974). Three-Dimensional Reconstruction in Nuclear Medicine Emission Imaging. IEEE Trans. Nucl. Sci., vol.21, pp.2–20.

    Google Scholar 

  • Dempster A, Laird N and Rubin D (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. J. R. Stat. Soc., vol.39, series B, pp.1–38.

    Google Scholar 

  • Derin H and Elliott H (1987). Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields. IEEE Trans. Pattern Anal. Machine Intell., vol.9, pp.39–55.

    Google Scholar 

  • Efron B (1986). How Biased is the Apparent Error Rate of a Prediction Rule. J. Am. Stat. Assoc., vol.81, pp.461–470.

    Google Scholar 

  • Gilland D, Jaszczak R, Greer K. Coleman R (1991). Quantitative SPECT Reconstruction of Iodine-123 Data. J. Nucl. Medicine, to appear.

    Google Scholar 

  • Goris M and Briandet P (1983). A Clinical and Mathematical Introduction to Computer Processing of Scintigraphic Images. Raven Press, New York.

    Google Scholar 

  • Hall E (1979). Computer Image Processing and Recognition. Academic Press, New York.

    Google Scholar 

  • Herman G (1980). Image Reconstruction from Projections: the fundamentals of computerized tomography. Academic Press, New York.

    Google Scholar 

  • Johnson V (1989). Bayesian Restoration of PET Images Using Gibbs Priors. In: Information Processing in Medical Imaging, vol.11, Berkerley, CA.

    Google Scholar 

  • Kullback S (1959). Information Theory and Statistics. John Wiley & Sons, Inc., New York.

    Google Scholar 

  • Lei T and Sewchand W (1988). A New Stochastic Model-Based Image Segmentation Technique for X-ray CT Images. In: SPIE, Visual Communication and Image Processing 3, Boston, MA.

    Google Scholar 

  • Lei T and Sewchand W (1989). Image Segmentation by Using Finite Normal Mixture Model and EM Algorithm. In: IEEE Intern. Conf. on Image Processing, Singapore.

    Google Scholar 

  • Liang Z and Hart H (1987). Bayesian Image Processing of Data from Constrained Source Distributions: fuzzy pattern constraints. Phys. Med. Biol., vol.32, pp.1481–1494.

    Google Scholar 

  • Liang Z and Hart H (1988a). Source Continuity and Boundary Discontinuity Considerations in Bayesian Image Processing. Med. Physics, vol.15, pp.754–756.

    Google Scholar 

  • Liang Z and Hart H (1988b). Bayesian Reconstruction in Emission Computerized Tomography. IEEE Trans. Nucl. Sci., vol.35, pp.877–885.

    Google Scholar 

  • Liang Z, Jaszczak R, Coleman R and Johnson V (1991a). Simultaneous Reconstruction, Segmentation, and Edge Enhancement of Relatively Piecewise Continuous Images with Intensity-Level Information. Med. Physics, vol.18, in press.

    Google Scholar 

  • Liang Z, Gilland, Jaszczak R and Coleman R (1991b). Implementation of Non-Linear Filters for Iterative Penalized Maximum Likelihood Image Reconstruction. In: IEEE Conf. Med. Imaging, Arlington, VA.

    Google Scholar 

  • Luenberger D (1973). Introduction to Linear and Non-Linear Programming. Addison-Wesley, Reading, MA.

    Google Scholar 

  • Margulis A and Gooding C (1989). Diagnostic Radiology. J. B. Lippincott Comp., Philadelphia.

    Google Scholar 

  • Pohost G, Higgins C, Morganroth J, Ritchie J and Schelbert H (1989). New Concepts in Cardiac Imaging. Year Book Med. Publish., Inc., Chicago.

    Google Scholar 

  • Redner R and Walker H (1984). Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM Review, vol.26, pp.195–239.

    Google Scholar 

  • Rissanen J (1978). Modeling by Shortest Data Description. Automatica, vol.14, pp.465–471.

    Google Scholar 

  • Schwarz G (1978). Estimating the Dimension of a Model. Annal. Stat., vol.6, pp.461–464.

    Google Scholar 

  • Shepp L and Vardi Y (1982). Maximum Likelihood Reconstruction for Emission Tomography. IEEE Trans. Med. Imaging, vol.1, pp.113–122.

    Google Scholar 

  • Stone M (1979). Comments on Model Selection Criteria of Akaike and Schwarz. J. Royal Statist. Soc., vol.41, pp.276–278.

    Google Scholar 

  • Titterington T, Smith A and Makov V (1985). Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons, Inc., New York.

    Google Scholar 

  • Young T and Fu K (1986). Handbook of Pattern Recognition and Image Processing. Academic Press, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Alan C. F. Colchester David J. Hawkes

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liang, Z., Jaszczak, R., Coleman, R. (1991). On reconstruction and segmentation of piecewise continuous images. In: Colchester, A.C.F., Hawkes, D.J. (eds) Information Processing in Medical Imaging. IPMI 1991. Lecture Notes in Computer Science, vol 511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033745

Download citation

  • DOI: https://doi.org/10.1007/BFb0033745

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54246-9

  • Online ISBN: 978-3-540-47521-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics