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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Hanselmann, M., Köthe, U., Renard, B. Y., Kirchner, M., Heeren, R. M. A., & Hamprecht, F. A. (2009). Multivariate watershed segmentation of compositional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5810 LNCS, pp. 180–192). https://doi.org/10.1007/978-3-642-04397-0_16
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