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Automated segmentation of brain exterior in MR images driven by empirical procedures and anatomical knowledge

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

This work demonstrates encouraging initial results for increasing the automation of a practical and precise MR brain image segmentation method. The intensity threshold for segmenting the brain exterior is automatically determined by locating the choroid plexus. This is done by finding peaks in a series of histograms taken over regions specified using anatomical knowledge. Intensity inhomogeneities are accounted for by adjusting the global intensity to match the white matter peak intensity in local regions. The results from 20 different brain scans (over 1000 images) obtained under different conditions are presented to validate the method which was able to determine the appropriate threshold in approximately 80% of the data.

Keywords

  • Choroid Plexus
  • Cerebral Spinal Fluid
  • Obsessive Compulsive Disorder
  • Intensity Inhomogeneity
  • Global Threshold

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.

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References

  1. V. S. Caviness, Jr., P. A. Filipek, and D. N. Kennedy, “Magnetic resonance technology in human brain science: A blueprint for a program based upon morphometry,” Brain Dev, vol. 11, pp. 1–13, 1989.

    PubMed  Google Scholar 

  2. V. S. Caviness, Jr., P. A. Filipek, and D. N. Kennedy, “Quantitative magnetic resonance imaging and studies of degenerative diseases of the developing human brain,” Brain-Dev., no. 14 Suppl, pp. S80–5, 1992.

    Google Scholar 

  3. P. Filipek, C. Richelme, D. Kennedy, J. Rademacher, D. Pitcher, S. Zidel, and V. C. Jr., “Morphometric analysis of the brain in developmental language disorders and autism,” Ann Neurol., vol. 32, p. 475, 1992.

    Google Scholar 

  4. M. Ashtari, J. Zito, B. Gold, J. Lieberman, Borenstein, and P. Herman, “Computerized volume measurement of brain structure,” Invest Rad, vol. 25, pp. 798–805, 1990.

    Google Scholar 

  5. C. Jack, F. Sharbrough, C. Twomey, G. Cascino, K. Hirschorn, W. Marsh, A. Zinsmeister, and B. Scheithaure, “Temporal Lobe Seizures: Lateralization with MR Volume Measurements of the Hippocampal Formation,” Radiology, pp. 423–429, 1990.

    Google Scholar 

  6. P. Scheltens, D. Leys, F. Barkhof, and e. al., “Atrophy of the medial temporal lobes on MRI in probable Alzheimer's disease and normal ageing: Diagnostic value and neuropsychological correlates,” J. Neurol Neurosurg Psychiatry, vol. 55, pp. 967–72, 1992.

    PubMed  Google Scholar 

  7. J. Seab, W. Jagust, S. Wong, M. Roos, B. Reed, and T. Budinger, “Quantitative NMR measurements of hippocampal in Alzheimer's disease,” Mag Res Med, vol. 8, pp. 200–8, 1988.

    Google Scholar 

  8. R. Duara, A. Kushch, K. Gross-Glen, and e. al., “Neuroanatomic differences between dyslexic and normal readers on MRI scans,” Arch Neurol, vol. 48, pp. 410–16, 1991.

    PubMed  Google Scholar 

  9. G. Hynd, M. Semrud-Clikeman, A. Lorys, and e. al., “Brain morphology in developmental dyslexia and attention deficit disorder,” Arch Neurol, vol. 47, pp. 919–26, 1990.

    PubMed  Google Scholar 

  10. G. Hynd, M. Semrud-Clikeman, A. Lorys, and e. al., “Corpus callosum morphology in attention deficit disorder (ADHD): Morphometric analysis of MRI,” J Learn Disab, vol. 24, pp. 121–46, 1991.

    Google Scholar 

  11. F. X. Castellanos, J. N. Giedd, W. L. Marsh, S. D. Hamburger, A. C. Vaituzis, D. P. Dickstein, S. E. Sarfatti, Y. C. Vauss, J. W. Snell, N. Lange, D. Kaysen, A. L. Krain, G. F. Ritchie, J. C. Rajapakse, and J. L. Rapoport, “Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder,” Archives of General Psychiatry, vol. 53, pp. 607–616, 1996.

    PubMed  Google Scholar 

  12. P. A. Filipek, M. Semrud-Clikeman, R. J. Steingard, P. F. Renshaw, D. N. Kennedy, and J. Biederman, “Volumetric MRI Analysis Comparing Attention-Deficit Hyperactivity Disorder and Normal Controls,” Annals of Neurology, in press.

    Google Scholar 

  13. M. E. Shenton, R. Kikinis, F. A. Jolesz, S. D. Pollak, M. LeMay, C. G. Wible, H. Hokama, J. Martin, D. Metcalf, M. Coleman, and et al., “Abnormalities of the left temporal lobe and thought disorder in schizophrenia,” N-Engl-J-Med., vol. 327, no. 9, pp. 604–12, 1992.

    PubMed  Google Scholar 

  14. D. Wicks, P. Tofts, D. Miller, and e. al., “Volume measurement of multiple sclerosis lesions with magnetic resonance images: A preliminary study,” Neuroradiology, vol. 34, pp. 475–9, 1992.

    PubMed  Google Scholar 

  15. J. Simon, R. Schiffer, R. Rudick, and R. Herndon, “Quantitative determination of MS-induced corpus callosum atrophy in vivo using MR imaging,” AJNR, vol. 8, pp. 599–604, 1987.

    PubMed  Google Scholar 

  16. G. J. Harris, G. D. Pearlson, C. E. Peyser, E. H. Aylward, J. Roberts, P. E. Barta, G. A. Chase, and S. E. Folstein, “Putamen volume reduction on magnetic resonance imaging exceeds caudate changes in mild Huntington's disease,” Ann-Neurol., vol. 31, no. 1, pp. 69–75, 1992.

    PubMed  Google Scholar 

  17. H. Breiter, P. Filipek, D. Kennedy, and e. al., “Pronounced white matter abnormalities in patients with obsessive compulsive disorder,” in Paper presented at Boston Society of Neurology and Psychiatry, 1992,.

    Google Scholar 

  18. D. N. Kennedy, P. A. Filipek, and V. S. Caviness, Jr., “Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging,” IEEE Transactions on Medical Imaging, vol. 8, no. 1, pp. 1–7, 1989.

    Google Scholar 

  19. J. Rademacher, A. Galaburda, D. Kennedy, P. Filipek, and V. C. Jr., “Human cerebral cortex: Localization, parcellation and morphometry with magnetic resonance imaging,” J Cog Neurosci., vol. 4, pp. 352–74, 1992.

    Google Scholar 

  20. D. Kennedy, P. Filipek, and V. C. Jr., “Fourier shape analysis of anatomic structures,” in Recent Advances in Fourier Analysis and its Applicaitons, NATO ASI Series. Dordrecht, The Netherlands: Kleuwer Academic Publishers, 1990, pp. 17–287.

    Google Scholar 

  21. A. J. Worth, N. Makris, M. R. Patti, J. M. Goodman, E. A. Hoge, V. S. Caviness, Jr., and D. N. Kennedy, “Precise Segmentation of the Lateral Ventricles and Caudate Nucleus in MR Brain Images using Anatomically Driven Histograms,” IEEE Transactions on Medical Imaging, (submitted).

    Google Scholar 

  22. P. A. Filipek, C. Richelme, D. N. Kennedy, and V. S. Caviness, Jr., “The young adult human brain: an MRI-based morphometric analysis,” Cereb-Cortex., vol. 4, no. 4, pp. 344–60, 1994.

    PubMed  Google Scholar 

  23. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek, “A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 672–82, 1992.

    Google Scholar 

  24. J. C. Bezdek, L. O. Hall, and L. P. Clarke, “Review of MR image segmentation techniques using pattern recognition,” Medical Physics, vol. 20, no. 4, pp. 1033–1048, 1993.

    PubMed  Google Scholar 

  25. A. P. Zijdenbos and B. M. Dawant, “Brain segmentation and white matter lesion detection in MR images,” Critical Reviews in Biomedical Engineering, vol. 22, no. 5–6, pp. 401–65, 1994.

    PubMed  Google Scholar 

  26. L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, and M. L. Silbiger, “MRI segmentation: Methods and applications,” Magnetic Resonance Imaging, vol. 13, no. 3, pp. 343–368, 1995.

    PubMed  Google Scholar 

  27. J. C. Russ, The image processing handbook, 2nd ed. Boca Raton, Florida: CRC Press, Inc., 1995.

    Google Scholar 

  28. S. Chua and P. McKenna, “Schizophrenia — a Brain Disease? A Critical Review of Structural and Functional Cerebral Abnormalidy in the Disorder,” British Journal of Psychiatry, vol. 166, pp. 563–582, 1995.

    PubMed  Google Scholar 

  29. R. G. Petty, P. E. Barta, G. D. Perlson, I. K. McGilcrist, R. W. Lewis, A. Y. Tien, A. Pulver, D. D. Vaughn, M. F. Casanova, and R. E. Powers, “Reversal of Asymmetry of the Planum Temporale in Schizophrenia,” American Journal of Psychiatry, vol. 152, no. 5, pp. 715–721, 1995.

    PubMed  Google Scholar 

  30. M. S. Buchsbaum, T. Someya, C. Y. Teng, L. Abel, S. Chin, A. Najafi, R. J. Haier, J. We, and W. E. Bunney, “PET and MRI of the Thalams in Never-Medicated Patients with Schizophrenia,” American Journal of Psychiatry, vol. 153, no. 2, p. 191–199, 1996.

    PubMed  Google Scholar 

  31. J. N. Giedd, J. W. Snell, N. Lange, J. C. Rajapakse, B. J. Casey, P. L. Kozuch, A. C. Vaituzis, Y. C. Vauss, S. D. Hamburger, D. Kaysen, and J. L. Rapoport, “Quantative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18,” Cerebral Cortex, vol. 6, pp. 1047–3211, 1996.

    Google Scholar 

  32. A. P. Zijdenbos, B. M. Dawant, and R. A. Margolin, “Inter-and Intra-Slice Intensity Correction in MR Images,” Proc Information Processing in Medical Imaging, vol. 14, pp. 349–350, 1995.

    Google Scholar 

  33. P. H. Bland and C. R. Meyer, “Robust three-dimensional object definition in CT and MRI,” Medical Physics, vol. 23, no. 1, pp. 99–107, 1996.

    PubMed  Google Scholar 

  34. W. M. Wells, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, “Adaptive Segmentation of MRI Data,” IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 429–442, 1996.

    Google Scholar 

  35. J. C. Rajapakse, J. N. Giedd, and J. L. Rapoport, “Statistical approach to segmentation of single-chhannel cerebral MR images,” IEEE Transactions on Medical Imaging, (in press).

    Google Scholar 

  36. E. A. Ashton, M. J. Berg, K. J. Parker, J. Weisberg, C. Chang Wen, and L. Ketonen, “Segmentation and feature extraction techniques, with applications to MRI head studies,” Magnetic Resonance in Medicine, vol. 33, no. 5, pp. 670–7, 1995.

    PubMed  Google Scholar 

  37. A. Lundervold and G. Storvik, “Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 14, no. 2, pp. 339–349, 1995.

    Google Scholar 

  38. C. Tsai, B. S. Manjunath, and R. Jagadeesan, “Automated segmentation of brain MR images,” Pattern Recognition, vol. 28, no. 12, pp. 1825–37, 1995.

    Google Scholar 

  39. M. Sonka, S. K. Tadikonda, and S. M. Collins, “Knowledge-Based Interpretation of MR Brain Images,” IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 443–452, 1996.

    Google Scholar 

  40. J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain: New York: Thieme Medical Publishers, Inc., 1988.

    Google Scholar 

  41. P. Filipek, D. Kennedy, and V. Caviness, “Volumemetric analysis of central nervous system neoplasms based on MRI,” Pediatric Neurology, vol. 7, pp. 347–51, 1991.

    PubMed  Google Scholar 

  42. N. Makris, “Theoretical segmentation mechanism for identifying periventricular structures in the human central nervous system,” Boston University, Department of Biomedical Engineering, BE 515 Term Paper, Boston, MA, (unpublished report) Spring 1995.

    Google Scholar 

  43. M. E. Brummer, R. M. Mersereau, R. L. Eisner, and R. R. J. Lewine, “Automatic detection of brain contours in MRI data sets,” IEEE Transactions on Medical Imaging, vol. 12, no. 2, pp. 153–66, 1993.

    Google Scholar 

  44. A. J. Worth and D. N. Kennedy, “Segmentation of magnetic resonance brain images using analogue constraint satisfaction neural networks,” Image and Vision Computing, vol. 12, no. 6, pp. 345–354, 1994.

    Google Scholar 

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Worth, A.J., Makris, N., Meyer, J.W., Caviness, V.S., Kennedy, D.N. (1997). Automated segmentation of brain exterior in MR images driven by empirical procedures and anatomical knowledge. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_8

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