Multivariate watershed segmentation of compositional data

Authors:
Michael Hanselmann;Ullrich Köthe;Bernhard Y. Renard;Marc Kirchner;Ron M. A. Heeren;Fred A. Hamprecht
Affiliations:
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany;Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany;Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany;Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany;FOM-Institute for Atomic and Molecular Physics, Amsterdam, The Netherlands;Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany
Venue:
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Year:
2009

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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.