Multivariate Watershed Segmentation of Compositional Data

  • Michael Hanselmann
  • Ullrich Köthe
  • Bernhard Y. Renard
  • Marc Kirchner
  • Ron M. A. Heeren
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5810)

Abstract

Watershed segmentation of spectral images is typically achieved by first transforming the high-dimensional input data into a scalar boundary indicator map which is used to derive the watersheds. We propose to combine a Random Forest classifier with the watershed transform and introduce three novel methods to obtain scalar boundary indicator maps from class probability maps. We further introduce the multivariate watershed as a generalization of the classic watershed approach.

Keywords

Compositional Data Imaging Mass Spectrometry Watershed Algorithm Watershed Segmentation Basin Type 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michael Hanselmann
    • 1
  • Ullrich Köthe
    • 1
  • Bernhard Y. Renard
    • 1
  • Marc Kirchner
    • 1
  • Ron M. A. Heeren
    • 2
  • Fred A. Hamprecht
    • 1
  1. 1.Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergGermany
  2. 2.FOM-Institute for Atomic and Molecular PhysicsAmsterdamThe Netherlands

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