Multivariate Watershed Segmentation of Compositional Data

Michael Hanselmann, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, Fred A. Hamprecht

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.