Segmentation of White Matter Lesions from Volumetric MR Images

  • S. A. Hojjatoleslami
  • F. Kruggel
  • D. Y. von Cramon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1679)


Quantitative analysis of the changes to the brain’s white matter is an important objective for a better understanding of pathological changes in various forms of degenerative brain diseases. To achieve an accurate quantification, an algorithm is proposed for automatic segmentation of white matter atrophies and lesions from T1-weighted 3D Magnetic Resonance (MR) images of the head. Firstly, white matter, gray matter and cerebrospinal fluid (CSF) compartments are segmented. Then, external and internal cisterns are separated by placing cutting planes relative to the position of the anterior and posterior commissure. Finally, a region growing method is applied to detect lesions inside the white matter. Since lesions may be adjacent to the gray matter, we use the external cisterns as a clue to prevent the algorithm from absorbing low gray level points in the gray matter.

The method is fully applied to detect the white matter lesions and relevant structures from a set of 41 MR images of normal and pathological subjects. Subjective assessment of the results demonstrates a high performance and reliability of this method.


White Matter Gray Matter White Matter Lesion Multiple Sclerosis Lesion Image Segmentation Technique 
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.


  1. 1.
    Atkins, M.S., Mackiewich, B.T.: Fully automatic segmentation of the brain in mri. IEEE Trans. Med. Imag. 17(1), 98–107 (1998)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of mr image segmentation techniques using pattern recognition. Med. Phys. 20(4), 1033–1048 (1993)CrossRefGoogle Scholar
  3. 3.
    Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall, L.O., Thatcher, R.W., Silbiger, M.L.: Mri segmentation: Methods and applications. Magn. Reson. Imag. 13(3), 343–368 (1995)CrossRefGoogle Scholar
  4. 4.
    Rajapakse, J.C., Kruggel, F.: Segmentation of mr images with intensity inhomogeneities. Image and Vision Computing 16, 165–180 (1998)CrossRefGoogle Scholar
  5. 5.
    Pachai, C., Zhu, Y.M., Grimaud, J., Hermier, M., Dromigny-Badin, A., Boudraa, A., Gimenez, G., Confavreux, C., Froment, J.C.: A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain mri. Computerized Med. Imag. and Graph 22, 399–408 (1998)CrossRefGoogle Scholar
  6. 6.
    Johnston, B., Atkins, M.S.: Segmentation of multiple sclerosis lesions in intensity corrected multispectral mri. IEEE Trans. Med. Imag. 15(2), 154–167 (1996)CrossRefGoogle Scholar
  7. 7.
    Udupa, B., Wei, S., Samarasekera, L., Miki, Y., Van Bucchem, M.A.: Multiple sclerosis lesion quantification using fuzzy-connectness principles. IEEE: Trans on Med. Imag. 16(5), 598–609 (1997)CrossRefGoogle Scholar
  8. 8.
    Kamber, M., Shinghal, R., Collins, L., Francis, G.S., Evans, A.C.: Model based 3- d segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Trans. Med. Imag. 14(3), 442–453 (1995)CrossRefGoogle Scholar
  9. 9.
    Haralik, R.M., Shapiro, L.G.: Survey: Image segmentation techniques. Comput. Vision, Graphics, Image Processing 29, 100–132 (1985)CrossRefGoogle Scholar
  10. 10.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26, 1277–1294 (1993)CrossRefGoogle Scholar
  11. 11.
    Hojjatoleslami, S.A., Kittler, J.: Region growing: A new approach. IEEE Trans Image Proc 7(7), 1079–1084 (1998)CrossRefGoogle Scholar
  12. 12.
    Hojjatoleslami, S.A., Kruggel, F., von Cramon, D.Y.: A region based algorithm for brain segmentation in mri, submitted (1999)Google Scholar
  13. 13.
    Serra, J.: Image analysis and mathematical morphology: theoretical advances, vol. 2. Academic Press, New York (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • S. A. Hojjatoleslami
    • 1
  • F. Kruggel
    • 1
  • D. Y. von Cramon
    • 1
  1. 1.Max-Planck-Institute of Cognitive NeuroscienceLeipzigGermany

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