Segmenting Multiple Sclerosis Lesions Using a Spatially Constrained K-Nearest Neighbour Approach

  • Mark Lyksborg
  • Rasmus Larsen
  • Per Soelberg Sørensen
  • Morten Blinkenberg
  • Ellen Garde
  • Hartwig R. Siebner
  • Tim Bjørn Dyrby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classification. These are used as a non parametric likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. The formulation is solved using the method of Iterated Conditional Modes (ICM). The parameters of the method are found through leave-one-out cross validation on training data after which it is evaluated on previously unseen test data. The multi modal features investigated are 3 structural MRI modalities, the diffusion MRI measures of Fractional Anisotropy (FA), Mean Diffusivity (MD) and several spatial features. Results show a benefit from the inclusion of diffusion primarily to the most difficult cases. Results shows that combining probabilistic K-Nearest Neighbour with a Markov Random Field formulation leads to a slight improvement of segmentations.


Fractional Anisotropy Near Neighbour Markov Random Field Multiple Sclerosis Lesion Iterate Conditional Mode 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Filippi, M., Horsfield, M.A., Morrissey, S.P., MacManus, D.G., Rudge, P., McDonald, W.I., Miller, D.H.: Quantitative brain MRI lesion load predicts the course of clinically isolated syndromes suggestive of multiple sclerosis. Neurology 44(4), 635–641 (1994)CrossRefGoogle Scholar
  2. 2.
    Filippi, M., Cercignani, M., Inglese, M., Horsfield, M.A., Comi, G.: Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 56(3), 304–311 (2001)CrossRefGoogle Scholar
  3. 3.
    Van Leemput, K., Maes, F., Vandermmeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2004)CrossRefGoogle Scholar
  4. 4.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)Google Scholar
  5. 5.
    Anbeek, P., Vincken, K.L., van Osch, M.J.P., Bisschops, R.H.C., van der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21, 1037–1044 (2004)CrossRefGoogle Scholar
  6. 6.
    Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistics Society B 48, 259–302 (1986)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Johnston, B., Atkins, M.S., Booth, K.S.: Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images. In: Proceedings of SPIE: Medical Imaging 1994, vol. 2167, pp. 28–39 (1994)Google Scholar
  8. 8.
    Basser, P.J., Mattiello, J., Le Bihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)CrossRefGoogle Scholar
  9. 9.
    Dice, L.R.: Measures of the Amount of Ecologic Association Between Species. Ecology 26 (3), 297–302 (1945)CrossRefGoogle Scholar
  10. 10.
    Reese, T.G., Heid, O., Weisskoff, R.M., Wedeen, V.J.: Reduction of eddy-current-induced distortion in diffusion mri using a twice-refocused spin echo. Magn. Reson. Med. 49, 177–182 (2003)CrossRefGoogle Scholar
  11. 11.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data. IEEE Trans. on Medical Imaging 17, 87–97 (1998)CrossRefGoogle Scholar
  12. 12.
    Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated Multi-modality Image Registration Based On Information Theory. Medical Imaging, 263–274 (1995)Google Scholar
  13. 13.
    Jezzard, P., Balaban, R.S.: Correction for geometric distortion in echo planar images from Bo field variations. Magn. Reson. Med. 34(1), 65–73 (1995)CrossRefGoogle Scholar
  14. 14.
    Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. on Medical Imaging 20(11), 1131–1139 (2001)CrossRefGoogle Scholar
  15. 15.
    Cook, P.A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C.: Camino: Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, pp. 27–59 (2006)Google Scholar
  16. 16.
    Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. IEEE Trans. on Information Theory 13, 21–27 (1967)zbMATHCrossRefGoogle Scholar
  17. 17.
    Muja, M., Lowe, D.G.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: Int. Conf. on Computer Vision Theory and Application, VISSAPP 2009, pp. 331–340 (2009)Google Scholar
  18. 18.
    Dyrby, T.B., Rostrup, E., Baare, F.C., Straaten, E.C.W., Barkhof, F., Vrenken, H., Ropele, S., Schmidt, R., Erkinjuntti, T., Wahlund, L.O., Pantoni, L., Inzitari, D., Paulson, O.B., Hansen, L.K., Waldemar, G.: Segmentation of age related white matter changes in a clinical multi-center study. NeuroImage 41, 335–345 (2008)CrossRefGoogle Scholar
  19. 19.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mark Lyksborg
    • 1
  • Rasmus Larsen
    • 1
  • Per Soelberg Sørensen
    • 3
  • Morten Blinkenberg
    • 3
  • Ellen Garde
    • 2
  • Hartwig R. Siebner
    • 2
  • Tim Bjørn Dyrby
    • 2
  1. 1.Informatics and Mathematical ModelingTechnical University of DenmarkDenmark
  2. 2.Danish Research Centre for Magnetic ResonanceCopenhagen University HospitalHvidovreDenmark
  3. 3.Danish Multiple Sclerosis Research CenterUniversity of CopenhagenDenmark

Personalised recommendations