Skip to main content
SpringerLink
Log in

ANIMAL+INSECT: Improved Cortical Structure Segmentation

  • 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

An algorithm for improved automatic segmentation of gross anatomical structures of the human brain is presented that merges the output of a tissue classification process with gross anatomical region masks, automatically defined by non-linear registration of a given data set with a probabilistic anatomical atlas. Experiments with 20 real MRI volumes demonstrate that the method is reliable, robust and accurate. Manually and automatically defined labels of specific gyri of the frontal lobe are similar, with a Kappa index of 0.657.

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

Access this chapter

Log in via an institution
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. S. P. Raya, “Low-level segmentation of 3D magnetic resonance brain images-a rule based system,” IEEE Transactions on Medical Imaging, vol. 9, pp. 327–337, Sept. 1990.

    Article  Google Scholar 

  2. L.-S. Chen and M. R. Sontag, “Representation, display and manipulation of 3D digital scenes and their medical applications,” Computer Vision, Graphics, and Image Processing, vol. 48, pp. 190–216, 1989.

    Article  Google Scholar 

  3. S. Dellepiane, S. Serpico, and G. Vernazza, “Approximate reasoning and knowledge in NMR image understanding,” in 8th International Conference on Pattern Recognition, (Paris, France), pp. 943–946, IEEE, Oct. 1986.

    Google Scholar 

  4. L. Arata, A. P. Dhawan, J. Broderick, and M. Gaskil, “Model-based analysis of MR images of the brain,” IEEE Engineering in Medicine and Biology, vol. 13, no. 3, pp. 1331–1332, 1991.

    Google Scholar 

  5. A. P. Dhawan and L. Arata, “Knowledge-based multi-modality three-dimensional image analysis of the brain,” American Journal of Physiologic Imaging, vol. 7, pp. 210–9, Jul–Dec 1992.

    Google Scholar 

  6. D. N. Davis and C. J. Taylor, “A blackboard architecture for automating cephalometric analysis,” Med Inf (Lond), vol. 16, pp. 137–49, Apr–Jun 1991.

    Google Scholar 

  7. Y. Kaneda, S. Fujii, Y. Kobashiri, M. Yoshirda, and M. Matsuo, “Pattern recognition and three dimensional construction from CT images,” in Proceedings of the International Conference on Cybernetics and Society, (Tokyo and Kyoto, Japan), pp. 281–284, IEEE, Nov 3–7 1978.

    Google Scholar 

  8. M. E. Brummer, “Hough transform detection of the longitudinal fissure in tomographic head images,” IEEE Transactions on Medical Imaging, vol. 10, Mar. 1991.

    Google Scholar 

  9. M. E. Brummer, R. M. Mersereau, R. L. Eisner, and R. R. J. Lewine, “Automatic detection of brain contours in MRI data sets,” in Information Processing in Medical Imaging (A. C. F. Colchester and D. J. Hawkes, eds.), (Wye, UK), p. 188, IPMI, July 1991.

    Google Scholar 

  10. I. Kapouleas, “Automatic detection of white matter lesions in magnetic eesonance brain images,” Comput Methods Programs Biomed, vol. 32, pp. 17–35, May 1990.

    Article  Google Scholar 

  11. C. Broit, Optimal registration of deformed images. PhD thesis, University of Pennsylvania, Philadelphia, 1981.

    Google Scholar 

  12. R. Bajcsy and C. Broit, “Matching of deformed images,” in Proceedings of the 6th International Conference on Pattern Recognition, (Munich, Germany), pp. 351–353, IEEE, Oct 19–22 1982.

    Google Scholar 

  13. R. Dann, J. Hoford, S. Kovacic, M. Reivich, and R. Bajcsy, “Three-dimensional computerized brain atlas for elastic matching: Creation and initial evaluation,” in Medical Imaging II, (Newport Beach, Calif.), pp. 600–608, SPIE, Feb. 1988.

    Google Scholar 

  14. J. Gee, L. LeBriquer, and C. Barillot, “Probabilistic matching of brain images,” in Information Processing in Medical Imaging (Y. Bizais and C. Barillot, eds.), (Ile Berder, France), IPMI, Kluwer, July 1995.

    Google Scholar 

  15. M. Miller, Y. A. G.E. Christensen, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proceedings of the National Academy of Sciences, vol. 90, no. 24, pp. 11944–11948, 1990.

    Article  Google Scholar 

  16. G. Christensen, R. Rabbitt, and M. Miller, “3D brain mapping using a deformable neuroanatomy,” Physics in Med and Biol, vol. 39, pp. 609–618, 1994.

    Article  Google Scholar 

  17. G. Christensen, R. Rabbitt, and M. Miller, “Deformable templates using large deformation kinematics,” IEEE Transactions on Image Processing, vol. 5, no. 10, pp. 1435–1447, 1996.

    Article  Google Scholar 

  18. D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans, “Automatic 3D model-based neuroanatomical segmentation,” Human Brain Mapping, vol. 3, no. 3, pp. 190–208, 1995.

    Article  Google Scholar 

  19. H. Zachmann, “Interpretation of cranial MR-images using a digital atlas of the human head,” IEEE Transactions on Medical Imaging, pp. 99–110, 1991.

    Google Scholar 

  20. J. Talairach and P. Tournoux, Co-planar stereotactic atlas of the human brain: 3-Dimensional proportional system: an approach to cerebral imaging. Stuttgart, New York: Georg Thieme Verlag, 1988.

    Google Scholar 

  21. P. T. Fox, M. A. Mintun, E. M. Reiman, and M. E. Raichle, “Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted PET images,” Journal of Cerebral Blood Flow and Metabolism, vol. 8, pp. 642–653, 1988.

    Google Scholar 

  22. P. T. Fox, S. Mikiten, G. Davis, and J. L. Lancaster, “BrainMap: A database of functional brain mapping,” in Functional Neuroimaging, technical foundations (R. W. Thatcher, M. Hallett, T. Zeffiro, E. R. John, and M. Heurta, eds.), pp. 95–105, San Diego, Ca.: Academic Press, 1994.

    Google Scholar 

  23. A. Evans, D. Collins, and C. Holmes, “Automatic 3D regional MRI segmentation and statistical probability anatomy maps,” in Quantification of Brain Function: Tracer kinetics and image analysis in brain PET (T. Jones, ed.), pp. 123–130, 1995.

    Google Scholar 

  24. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A non-parametric method for automatic correction of intensity non-uniformity in MRI data,” IEEE Transactions on Medical Imaging, vol. 17, Feb. 1998.

    Google Scholar 

  25. D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans, “Automatic 3D intersubject registration of MR volumetric data in standardized talairach space,” Journal of Computer Assisted Tomography, vol. 18, pp. 192–205, March/April 1994.

    Article  Google Scholar 

  26. D. MacDonald, D. Avis, and A. C. Evans, “Multiple surface identification and matching in magnetic resonance images,” in Proceedings of Conference on Visualization in Biomedical Computing, SPIE, 1994.

    Google Scholar 

  27. D. MacDonald, Identifying geometrically simple surfaces from three dimensional data. PhD thesis, McGill University, Montreal, Canada, December 1994.

    Google Scholar 

  28. A. P. Zijdenbos, A. C. Evans, F. Riahi, J. Sled, J. Chui, and V. Kollokian, “Automatic quantification of multiple sclerosis lesion volume using stereotaxic space,” in Proceedings of the 4th International Conference on Visualization in Biomedical Computing, VBC’ 96:, (Hamburg), pp. 439–448, September 1996.

    Google Scholar 

  29. N. Kabani, D. Collins, and A. Evans, “A 3d neuroanatomical atlas,” in 4th International Conference on Functional Mapping of the Human Brain (A. Evans, ed.), (Montreal), Organization for Human Brain Mapping, June 1998. submitted.

    Google Scholar 

  30. A. C. Evans, D. L. Collins, and B. Milner, “An MRI-based stereotactic atlas from 250 young normal subjects,” Soc.Neurosci.Abstr., vol. 18, p. 408, 1992.

    Google Scholar 

  31. J. Mazziotta, A. Toga, A. Evans, P. Fox, and J. Lancaster, “A probabilistic atlas of the human brain: theory and rationale for its development. the international consortium for brain mapping,” NeuroImage, vol. 2, no. 2, pp. 89–101, 1995.

    Article  Google Scholar 

  32. M. Ono, S. Kubik, and C. Abernathey, Atlas of Cerebral Sulci. Stuttgart: Georg Thieme Verlag, 1990.

    Google Scholar 

  33. J. Absher, “A probabilistic atlas of the thalamus,” tech. rep., McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Sept 1993.

    Google Scholar 

  34. D. L. Collins, N. J. Kabani, and A. C. Evans, “Automatic volume estimation of gross cerebral structures,” in 4th International Conference on Functional Mapping of the Human Brain (A. Evans, ed.), (Montreal), Organization for Human Brain Mapping, June 1998. abstract no. 702.

    Google Scholar 

  35. W. Baar’e, D. Collins, N. Kabani, D. MacDonald, C. Liu, M. Petrides, R. Kwan, and A. Evans, “Automated and manual identification of frontal lobe gyri,” in Third International Conference on Functional Mapping of the Human Brain, vol. 5, (Copenhagen), p. S348, Human Brain Map, May 1997.

    Google Scholar 

  36. D. MacDonald, “Display: a user’s manual,” tech. rep., McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Sept 1996.

    Google Scholar 

  37. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945.

    Article  Google Scholar 

  38. A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: Method and validation,” IEEE Transactions on Medical Imaging, vol. 13, pp. 716–724, Dec. 1994.

    Article  Google Scholar 

  39. C. J, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurements, vol. 20, pp. 37–46, 1960.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montr’ eal Neurological Institute, McGill University, 3801 University St., Montr’ eal, Canada, H3A 2B4

    D. Louis Collins, Alex P. Zijdenbos & Alan C. Evans

  2. Dept. Psychiatry, University Hospital Utrecht, Utrecht, the Netherlands

    Wim F. C. Baaré

Authors
  1. D. Louis Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex P. Zijdenbos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wim F. C. Baaré
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan C. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, József Attila University, Árpád tér 2., H-6720, Szeged, Hungary

    Attila Kuba

  2. Institute of Nuclear Medicine, Charles University of Prague, Salmovska 3, CZ-120 00, Prague, Czech Republic

    Martin Šáamal

  3. Department of Medical Physics, University College London, Gower Street, London, WC1E 6BT, UK

    Andrew Todd-Pokropek

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., Evans, A.C. (1999). ANIMAL+INSECT: Improved Cortical Structure Segmentation. 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_16

Download citation

  • DOI: https://doi.org/10.1007/3-540-48714-X_16

  • 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

Search

Navigation