Advertisement

International Journal of Computer Vision

, Volume 31, Issue 2–3, pp 185–202 | Cite as

Multiscale Segmentation of Three-Dimensional MR Brain Images

  • W.J. Niessen
  • K.L. Vincken
  • J. Weickert
  • B.M. Ter Haar Romeny
  • M.A. Viergever
Article

Abstract

Segmentation of MR brain images using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A multiscale method to MRI brain segmentation is presented which uses both edge and intensity information. First a multiscale representation of an image is created, which can be made edge dependent to favor intra-tissue diffusion over inter-tissue diffusion. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of objects based on intensity. It is shown that both an improvement in accuracy and a reduction in image post-processing can be achieved if edge dependent diffusion is used instead of linear diffusion. The combination of edge dependent diffusion and intensity based linking facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. To segment the total brain (white matter plus grey matter) morphological operations are applied to remove small bridges between the brain and cranium. If the total brain is segmented, grey matter, white matter and cerebrospinal fluid can be segmented by joining a small number of segments. Using a supervised segmentation technique and MRI simulations of a brain phantom for validation it is shown that the errors are in the order of or smaller than reported in literature.

segmentation linear scale space nonlinear scale space 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acton, S.T., Bovik, A.C., and Crawford, M.M. 1994. Anisotropic diffusion pyramids for image segmentation. Proc. First International Conference on Image Processing, IEEE, pp. 478–482.Google Scholar
  2. Alvarez, L., Guichard, F., Lions, P.L., and Morel, J.M. 1993. Axioms and fundamental equations of image processing. Archives for Rational Mechanics, 123(3):199–257.Google Scholar
  3. Atkins, M.S. and Mackiewich, B.T. 1998. Fully automated segmentation of the brain in MRI. IEEE Transactions on Medical Imaging, 17(1):98–107.Google Scholar
  4. Bezdek, J.C., Hall, L.O., and Clarke, L.P. 1993. Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4):1033–1048.Google Scholar
  5. Bomans, M., Höhne, K, Tiede, U., and Riemer, M. 1990. 3-D segmentation of MR images of the head for 3-D display. IEEE Transactions on Medical Imaging, 9(2):177–183.Google Scholar
  6. Brummer, M.E., Mersereau, R.M., Eisner, R.L., and Lewine, R.R.J. 1992. Automatic detection of brain contours in MRI data sets. IEEE Transactions on Medical Imaging, 12(2):153–166.Google Scholar
  7. Caselles, V. and Sbert, C. 1997. What is the best causal scale space for 3-D images? SIAM Journal on Numerical Analysis, 56(4): 1199–1246.Google Scholar
  8. Catté, F., Lions, P.L., Morel, J.M., and Coll, T. 1992. Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis, 29(1):182–193.Google Scholar
  9. Chakraborty, A., Staib, L.H., and Duncan, J.S. 1996. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging, 15(6):859–870.Google Scholar
  10. Chu, C.C. and Aggarwal, J.K. 1993. The integration of image segmentation maps using region and edge information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(12): 1241–1252.Google Scholar
  11. Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall, L.O., Thatcher, R.W., and Silbiger, M.L. 1995. MRI segmentation: Methods and applications. Magn. Reson. Imag., 13(3):343–368.Google Scholar
  12. Cline, H.E., Lorensen, W.E., Kikinis, R., and Jolesz, F. 1990. Three dimensional segmentation of MR images of the head using probability and connectivity. Journal of Computer Assisted Tomography, 14(6):1037–1045.Google Scholar
  13. Collins, D.L., Peters, T.M., Dai, W., and Evans, A.C. 1992. Model based segmentation of individual brain structures from MRI data. In Proceedings Visualization in Biomedical Computing, R. Robb (Ed.), SPIE, pp. 10–23.Google Scholar
  14. Dai, D., Condon, B., Rampling, R., and Teasdale, G. 1993. Intracranial deformation caused by brain tumors; assessment of 3-D surface by magnetic resonance imaging. IEEE Transactions on Medical Imaging, 12(4):693–702.Google Scholar
  15. Davatzikos, C. and Bryan, R.N. 1996. Using a deformable surface model to obtain a shape representation of the cortex. IEEE Transactions on Medical Imaging, 15(6):785–795.Google Scholar
  16. Dawant, B.M., Zijdenbos, A.P., and Margolin, R.A. 1993. Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Transactions on Medical Imaging, 12(4): 770–781.Google Scholar
  17. Dziuk, G. and Kawohl, B. 1991. On rotationally symmetric mean curvature flow. Journal of Differential Equations, 93:142–149.Google Scholar
  18. Evans, L.C. and Spruck, J. 1991. Motion of level sets by mean curvature I. Journal of Differential Geometry, 33:635–681.Google Scholar
  19. Firey, W.J. 1974. Shape of worn stones. Mathematika, 21:1–11.Google Scholar
  20. Fletcher, L.M., Barsotti, J.B., and Hornak, J.P. 1993. A multispectral analysis of brain tissues. Magnetic Resonance in Medicine, 29:623–630.Google Scholar
  21. Florack, L.M.J., ter Haar Romeny, B.M., Koenderink, J.J., and Viergever, M.A. 1994. Linear scale-space. Journal of Mathematical Imaging and Vision, 4(4):325–351.Google Scholar
  22. Gerig, G., Martin, J., Kikinis, R., Kübler, O., Shenton, M., and Jolesz, F.A. 1992. Unsupervised tissue type segmentation of 3D dual-echo MR head data. Image and Vision Computing, 10(6):349–360.Google Scholar
  23. Grayson, M. 1989. A short note on the evolution of a surface by its mean curvature. Duke Mathematical Journal, 58:555–558.Google Scholar
  24. Griffin, L.D., Colchester, A.C.F., and Robinson, G.P. 1992. Structure-sensitive scale and the hierarchical segmentation of images. In Visualization in Biomedical Computing, R. Robb (Ed.), SPIE, vol. 1808, pp. 24–32.Google Scholar
  25. Griffin, L.D., Robinson, G.P., and Colchester, A.C.F. 1993. Multiscale hierarchical segmentation. British Machine Vision Conference, J. Illingworth (Ed.), BNVA Press, pp. 135–144.Google Scholar
  26. Grimson, W.E.L., Ettinger, G.J., White, S.J., Gleason, P.L., Pérez, L., Wells, W.M., and Kikinis, R. 1995. Evaluating and validating an automated registration system for enhanced reality visualization in surgery. In Proceedings CVRMed, N. Ayache (Ed.), volume 905 of Lecture Notes in Computer Science, Springer-Verlag: Berlin, pp. 3–12.Google Scholar
  27. Grimson, W.E.L., Ettinger, G.J., White, S.J., Gleason, P.L., Pérez, L., Wells, W.M., and Kikinis, R. 1996. An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization. IEEE Transactions on Medical Imaging, 15(2):129–140.Google Scholar
  28. Höhne, K.H. and Hanson, W.H. 1992. Interactive 3-D segmentation of MRI and CT volumes using morphological operations. Journal of Computer Assisted Tomography, 16(2):285–294.Google Scholar
  29. Huisken, G. 1984. Flow by mean curvature of convex surfaces into spheres. Journal of Differential Geometry, 20:237–266.Google Scholar
  30. Huisken, G. 1991. Asymptotic behaviour of singularities of the mean curvature flow. Journal of Differential Geometry, 31: 285–299.Google Scholar
  31. Johnston, B., Atkins, M.S., Mackiewich, B., and Anderson, M. 1996. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Transactions on Medical Imaging, 15(2):154–169.Google Scholar
  32. Kapur, T., Grimson, W.E.L., Wells, W.M., and Kikinis, R. 1996. Segmentation of brain tissue from Magnetic Resonance images. Medical Image Analysis, 1(2):109–127.Google Scholar
  33. Kennedy, D.N., Filipek, P.A., and Caviness, V.S. 1989. Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. IEEE Transactions on Medical Imaging, 8(1):1–7.Google Scholar
  34. Kikinis, R., Shenton, M.E., Gerig, G., Martin, J., Anderson, M., Metcalf, D., Guttmann, Ch. R.G., McCarley, R.W., Lorensen, B., Cline, H., and Jolesz, F.A. 1992. Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. Journal of Magnetic Resonance Imaging, 2(6):619–629.Google Scholar
  35. Koenderink, J.J. 1984. The structure of images. Biological Cybernetics, 50:363–370.CrossRefGoogle Scholar
  36. Koenderink, J.J. and van Doorn, A.J. 1986. Dynamic shape. Biological Cybernetics, 53:383–396.Google Scholar
  37. Kohn, M., Tanna, N., Herman, G., Resnick, S.M., Mozley, P.D., Gur, R.E., Alavi, A., Zimmerman, R.A., and Gur, R.C. 1991. Analysis of brain and cerebrospinal fluid volumes with MR imaging. Radiology, 178:115–122.Google Scholar
  38. Koster, A.S.E. 1995. Linking Models for Multiscale Image Segmentation. Ph.D. Thesis, Utrecht University, The Netherlands.Google Scholar
  39. Koster, A.S.E., Vincken, K.L., De Graaf, C.N., Zander, O.C., and Viergever, M.A. 1997. Heuristic linking models in multiscale image segmentation. Computer Vision and Image Understanding, 65(3):382–402.Google Scholar
  40. Li, C., Goldgof, B., and Hall, L.O. 1993. Knowledge-based classi-fication and tissue labeling of MR images of human brain. IEEE Transactions on Medical Imaging, 12(4):740–750.Google Scholar
  41. Lim, K.O. and Pfefferbaum, A. 1990. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. Journal of Computer Assisted Tomography, 13(4):588–593.Google Scholar
  42. Maes, F., Vandermeulen, D., Suetens, P., and Marchal, G. 1995. Computer-aided interactive object delineation using an intelligent paintbrush technique. In Proceedings CVRMed, N. Ayache (Ed.), volume 905 of Lecture Notes in Computer Science, Springer-Verlag: Berlin, pp. 77–83.Google Scholar
  43. McInerney, T. and Terzopoulos, D. 1996. Deformable models in medical image analysis: A survey. Medical Image Analysis, 1(2): 91–108.Google Scholar
  44. Neskovic, P. and Kimia, B.B. 1994. Three-dimensional shape representation from curvature dependent surface evolution. Proc. First International Conference on Image Processing, IEEE, pp. 6–10.Google Scholar
  45. Niessen, W.J., Vincken, K.L., and Viergever, M.A. 1996. Comparison of multiscale representations for a linking-based image segmentation model. Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, San Francisco, pp. 263–272.Google Scholar
  46. Niessen, W.J., Vincken, K.L., Weickert, J., ter Haar Romeny, B.M., and Viergever, M.A. n.d. Geodesic deformable models for medical image analysis. IEEE Transactions on Medical Imaging, 17(μ):634–641.Google Scholar
  47. Niessen, W.J., Vincken, K.L., Weickert, J., and Viergever, M.A. 1997. Nonlinear multiscale representations for image segmentation. Computer Vision and Image Understanding, 66(2):233–245.Google Scholar
  48. Osher, S. and Sethian, S. 1988. Fronts propagating with curvature dependent speed: Algorithms based on the Hamilton-Jacobi formalism. Journal of Computational Physics, 79:12–49.CrossRefGoogle Scholar
  49. Perona, P. and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629–639.CrossRefGoogle Scholar
  50. Peters, T., Davey, B., Munger, P., Comeau, R., Evans, A., and Olivier, A. 1996. Three-dimensional multimodal image-guidance for neurosurgery. IEEE Transactions on Medical Imaging, 15(2): 121–128.Google Scholar
  51. Raya, S.P. 1990. Low-level segmentation of 3-D Magnetic Resonance brain images: A rule-based system. IEEE Transactions on Medical Imaging, 9(3):327–337.Google Scholar
  52. Rusinek, H., deLeon, M.J., Goerge, A.E., Stylopoulos, L.A., Chandra, R., Smith, G., Rand, T., Mourino, M., and Kowalski, H. 1991. Alzheimer disease: Measuring loss of cerebral gray matter with MR imaging. Radiology, 178:109–114.Google Scholar
  53. Sandor, S. and Leahy, R. 1997. Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Transactions on Medical Imaging, 16(1):41–54.Google Scholar
  54. Sethian, J.A. 1990. Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws. Journal of Differential Geometry, 31:131–161.Google Scholar
  55. Shenton, M., Kikinis, R., Jolez, F.A., Pollak, S.D., LeMay, M., Wible, C.G., Hokama, H., Martin, J., Metcalf, D., Coleman, M., and McCarley, R.W. 1992. Abnormalities of the left temporal lobe and thought disorder in schizophrenia. New England Journal of Medicine, 327:604–612.Google Scholar
  56. Sled, J.G., Zijdenbos, A.P., and Evans, A.C. 1997. A comparison of retrospective intensity non-uniformity correction methods for MRI. In Proceedings Information Processing in Medical Imaging, J.S. Duncan and G. Gindi (Eds.), pp. 459–464.Google Scholar
  57. Sled, J.G., Zijdenbos, A.P., and Evans, A.C. 1998. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1):87–97.Google Scholar
  58. Sonka, M., Tadikonda, S.K., and Collins, S.M. 1996. Knowledgebased interpretation of MR brain images. IEEE Transactions on Medical Imaging, 15(4):443–452.Google Scholar
  59. Staib, L.H. and Duncan, J.S. 1996. Model-based deformable surface finding for medical images. IEEE Transactions on Medical Imaging, 15(5):720–731.Google Scholar
  60. Vannier, M., Butterfield, R., Jordon, D., Murphy, W., Levitt, R.G., and Gado, M. 1985. Multispectral analysis of magnetic resonance images. Radiology, 154:221–224.Google Scholar
  61. Vincken, K.L. 1995. Probabilistic Multiscale Image Segmentation by the Hyperstack. Ph.D. Thesis, Utrecht University, The Netherlands.Google Scholar
  62. Vincken, K.L., Koster, A.S.E., and Viergever, M.A. 1997. Probabilistic multiscale image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(19):109–120.Google Scholar
  63. Weickert, J. 1996. Anisotropic Diffusion in Image Processing. Ph.D. Thesis, Dept. of Mathematics, University of Kaiserslautern, Germany. Revised and extended version available as book (Teubner Verlag: Stuttgart, 1998).Google Scholar
  64. Weickert, J., ter Haar Romeny, B.M., and Viergever, M.A. 1998. Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, 7(3):398–410.CrossRefGoogle Scholar
  65. Wells, W., Kikinis, R., Grimson, E., and Jolesz, F. 1996. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15(4):429–442.CrossRefGoogle Scholar
  66. Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P., and Tannenbaum, A. 1997. A geometric snake model for segmentation of medical imagery. IEEE Transactions on Medical Imaging, 16(2):199–209.CrossRefGoogle Scholar
  67. Zhu, S.C. and Yuille, A. 1996. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9):884–900.CrossRefGoogle Scholar
  68. Zijdenbos, A., Dawant, B.M., and Margolin, R.A. 1994. Automatic detection of intracranial contours in MR images. Computerized Medical Imaging and Graphics, 18:11–23.CrossRefGoogle Scholar
  69. Zijdenbos, A., Dawant, B.M., Margolin, R.A., and Palmer, A.C. 1994. Morphometric analysis of white matter lesions in MR images. IEEE Transactions on Medical Imaging, 13(4):716–724.CrossRefGoogle Scholar
  70. Zuiderveld, K.J., Koning, A.H.J., Stokking, R., Maintz, J.B. Antoine, Appelman, Fred and Viergever, M.A. 1996. Multimodality visualization of medical volume data—our techniques, applications, and experiences. Computer and Graphics, 20(6):775–791.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • W.J. Niessen
    • 1
  • K.L. Vincken
    • 1
  • J. Weickert
    • 1
  • B.M. Ter Haar Romeny
    • 1
  • M.A. Viergever
    • 1
  1. 1.Image Sciences InstituteUniversity Hospital UtrechtThe Netherlands

Personalised recommendations