Skip to main content

A Hierarchical Feature Based Deformation Model Applied to 4D Cardiac SPECT Data

  • Conference paper
  • First Online:
Book cover Information Processing in Medical Imaging (IPMI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1613))

Abstract

In this paper we describe a statistical model for the observation of labeled points in gated cardiac single photon emission computed tomography (SPECT) images. The model has two major parts: one based on shape correspondence between the image for evaluation and a reference image, and a second based on the match in image features. While the statistical deformation model is applicable to a broad range of image objects, the addition of a contraction mechanism to the baseline model provides particularly convincing results in gated cardiac SPECT. The model is applied to clinical data and provides marked improvement in the quality of summary images for the time series. Estimates of heart deformation and contraction parameters are also obtained.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amit, Y., Grenander, U., Piccione, M.: Structural image restoration through deformable templates. Journal of the American Statistical Society 86 (1991) 376–387

    Google Scholar 

  2. Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society (series B) 48 (1986) 259–302

    MATH  MathSciNet  Google Scholar 

  3. Bookstein, F.: Morphometric tools for landmark data. Cambridge University Press, Cambridge (1991)

    MATH  Google Scholar 

  4. Clarysse, P., Friboulet, D., Magnin, I.E.: Tracking geometrical descriptors on 3-D deformable surfaces: Application to the left-ventricular surface of the heart. IEEE Transactions on Medical Imaging 16 (1997) 392–404

    Article  Google Scholar 

  5. Coffey, J.L., Cristy, M., Warner, G.: Specific absorbed fractions for photon sources uniformly distributed in the heart chambers and heart wall of a heterogeneous phantom. Journal of Nuclear Medicine (MIRD pamphlet no. 13) 22 (1980) 65–71

    Google Scholar 

  6. Collins, D., Holmes, C., Peters, T., Evans, A.: Automatic 3-d model-based neuroanatomical segmentation. Human Brain Mapping 3 (1995) 190–208

    Article  Google Scholar 

  7. Cootes, T.F., Hill, A., Taylor, C.J., Haslam, J.: The use of active shape models for locating structures in medical images. Image and Vision Computing 12 (1994) 355–366

    Article  Google Scholar 

  8. Davatzikos, C., Vaillant, M., Resnick, R.: A computerized approach for morphological analysis of the corpus callosum. Journal of Computer Assisted Tomography 20 (1996) 88–97

    Article  Google Scholar 

  9. Kass, M., Witkin, A., Terzopolous, D.: Snakes: active contour models. International Journal of Computer Vision (1988) 321–331

    Google Scholar 

  10. Klein, G.J., Reutter, B.W., Huesman, R.H.: Non-rigid summing of gated pet via optical flow. IEEE Transactions on Nuclear Science 44 (1997) 1509–1512

    Article  Google Scholar 

  11. Laading, J.K., McCulloch, C.C., Johnson, V.E.: A hierarchical object deformation model applied to the digital chest radiograph. In: The American Statistical Association Proceedings of the Section on Bayesian Statistical Science, Anaheim, California (1997)

    Google Scholar 

  12. Lindeberg, T.: Scale-space Theory. Kluwer Academic Publishers, Boston, MA (1994)

    Google Scholar 

  13. Luettgen, M.R., Karl, W.C., Willsky, A.S.: Efficient multiscale regularization with applications to the computation of optical flow. IEEE Transactions on Image Processing 70 (1994) 41–64

    Article  Google Scholar 

  14. McCulloch, C.C.: High-level image understanding via Bayesian hierarchical models. PhD thesis, Duke University (1998)

    Google Scholar 

  15. McCulloch, C.C., Laading, J.K., Johnson, V.E.: Image feature identiffication via bayesian hierarchical models. In: The American Statistical Association Proceedings of the Section on Bayesian Statistical Science, Anaheim, California (1997)

    Google Scholar 

  16. McCulloch, C.C., Laading, J.K., Wilson, A., Johnson, V.E.: A shape-based frame-work for automated image segmentation. In: The American Statistical Association Proceedings of the Section on Bayesian Statistical Science, Chicago, Illinois (1996) 1–6

    Google Scholar 

  17. McEachen, J.C., Duncan, J.S.: Shape-based tracking of left ventricular motion. IEEE Transactions on Medical Imaging 16 (1997) 270–283

    Article  Google Scholar 

  18. Nelder, J., Mead, R.: A simplex method for function minimization. The Computer Journal 7 (1965) 308–313

    MATH  Google Scholar 

  19. Park, J., Metaxas, D., Young, A.A., Axel, L.: Deformable models with parameter functions for cardiac motion analysis from tagged mri data. IEEE Transactions on Medical Imaging 15 (1996) 1–13

    Article  Google Scholar 

  20. Peter, J., Gilland, D.R., Jaszczak, R.J., Coleman, R.E.: Four-dimensional quadric-based cardiac-thorax phantom for monte carlo simulation of radiological imaging systems. Submitted to: IEEE Transactions on Nuclear Science (1998)

    Google Scholar 

  21. Potel, M.J., MacKay, S.A., Rubin, J.A., Aisen, A.M., Sayre, R.E.: Three-dimensional left ventricular wall motion in man. coordinate systems for representing wall movement direction. Investigative Radiology 19 (1984) 499–509

    Article  Google Scholar 

  22. terHaar Romeny, B.M., Florack, L., Koenderink, J., Viergever, M.: Scale-space: its natural operators and differential invariants. In: Lecture Notes in Computer Science 511 Springer-Verlag, Berlin, Germany (1991) 239–255

    Google Scholar 

  23. Young, R.: The gaussian derivative model for machine vision: I. retinal mechanisms. Spatial Vision 2 (1987) 273–293

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Laading, J.K., McCulloch, C., Johnson, V.E., Gilland, D.R., Jaszczak, R.J. (1999). A Hierarchical Feature Based Deformation Model Applied to 4D Cardiac SPECT Data. In: Kuba, A., Šáamal, M., Todd-Pokropek, A. (eds) Information Processing in Medical Imaging. IPMI 1999. Lecture Notes in Computer Science, vol 1613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48714-X_20

Download citation

  • DOI: https://doi.org/10.1007/3-540-48714-X_20

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66167-2

  • Online ISBN: 978-3-540-48714-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics