Fast Segmentation of Brain Magnetic Resonance Tomograms

  • Gangolf Mittelhäußer
  • Frithjof Kruggel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)


We describe a combination of a region growing and a watershed algorithm optimized for the detection of homogeneous structures in magnetic resonance (MR) volume datasets. No prior knowledge is used except a segment model. The adaptation to different data sets is controlled by parameters which can be determined interactively due to the high speed of the algorithm. Results are shown for the segmentation of the basal ganglia and the white matter of the brain.


White Matter Basal Ganglion Segment Model Gradient Magnitude Neighboring Segment 
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  1. 1.
    Kruggel F, Horsch A, Mittelhäußer G, Schnabel M: “Image Processing in the Neurologic Sciences”, Proc. IEEE Workshop on Biomed. Image Analysis, Seattle, June 1994, pp. 214–223Google Scholar
  2. 2.
    Vincent I, Soille P: “Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations”, IEEE PAM, Vol. 13, No. 6, June 1991, pp. 583–598CrossRefGoogle Scholar
  3. 3.
    Gambotto J P, Monga O: “A Parallel and Hierarchical Algorithm for Region Growing”, Proc. IEEE Conf. on Comp. Vision Pattern Recognition, 1985, pp. 649–652Google Scholar
  4. 4.
    Upson C, Keeler M: “V-Buffer: Visible Volume Rendering”, Computer Graphics, Vol. 22, No. 4, Aug. 1988, pp. 59–64CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Gangolf Mittelhäußer
    • 1
  • Frithjof Kruggel
    • 2
  1. 1.University of KaiserslauternKaiserslauternGermany
  2. 2.Technical University MunichMunichGermany

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