Comparison of traditional brain segmentation tools with 3D self-organizing maps

  • David Dean
  • Krishnamurthy Subramanyan
  • Janardhan Kamath
  • Fred Bookstein
  • David Wilson
  • David Kwon
  • Peter Buckley
Posters Segmentation/Structural Models
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1230)

Abstract

Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and self-organizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12∶1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from S 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.

Keywords

Attenuation 

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References

  1. 1.
    Chalana, V., Kim, Y.: A methodology of Image Segmentation Algorithms on Medical Images. In (M.H. Loew and K.M. Hanson, eds.) SPIE MT96 2710-20 (1996) 178–189.Google Scholar
  2. 2.
    Dean, D., Buckley, P., Bookstein, F., Kamath, J., Kwon, D., Friedman, L., and Lys, C.: Three Dimensional MR-based Morphometric Comparison of Schizophrenic and Normal Cerebral Ventricles. In (K.-H. Höhne and R. Kikinis, eds.) Visualization in Biomedical Computing '96. Lecture Notes in Computer Science. 1131 (1996) 363–372.Google Scholar
  3. 3.
    Joliot, M., Mazoyer, B.M.: Three-dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans. Med. Imaging 12 (1993) 269–277.Google Scholar
  4. 4.
    Kohonen, T.: Physiological interpretation of the self-organizing map. Neural Networks 6 (1993) 895–905.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • David Dean
    • 1
  • Krishnamurthy Subramanyan
    • 2
  • Janardhan Kamath
    • 2
  • Fred Bookstein
    • 4
  • David Wilson
    • 2
  • David Kwon
    • 3
  • Peter Buckley
    • 3
  1. 1.Department of Neurological SurgeryCase Western Reserve UniversityCleveland
  2. 2.Department of Biomedical EngineeringCase Western Reserve UniversityCleveland
  3. 3.Department of PsychiatryCase Western Reserve UniversityCleveland
  4. 4.Institute of GerontologyUniversity of MichiganAnn Arbor

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