ilastik is a simple, user-friendly tool for image classification and segmentation in up to three spatial and one spectral dimension. Using it requires no experience in image processing. ilastik has a convenient mouse interface for labeling an arbitrary number of classes in the images. These labels, along with a set of generic (nonlinear) image features, are then used to train a Random Forest classifier. In the interactive training mode, ilastik provides real-time feedback of the current classifier predictions and thus allows for targeted training and overall reduced labeling time. In addition, an uncertainty measure can guide the user to ambiguous regions of the data. Once the classifier has been trained on a representative subset of the data, it can be exported and used to automatically process a very large number of images.

References in zbMATH (referenced in 9 articles )

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  1. Tang, M. X.; Wu, Y.; Zong, H. H.; Luo, Y. H.; Yang, H. S.; Guo, S. G.: Experimental investigation of supersonic boundary-layer tripping with a spanwise pulsed spark discharge array (2022)
  2. Hahn, Artur; Bode, Julia; Krüwel, Thomas; Kampf, Thomas; Buschle, Lukas R.; Sturm, Volker J. F.; Zhang, Ke; Tews, Björn; Schlemmer, Heinz-Peter; Heiland, Sabine; Bendszus, Martin; Ziener, Christian H.; Breckwoldt, Michael O.; Kurz, Felix T.: Gibbs point field model quantifies disorder in microvasculature of U87-glioblastoma (2020)
  3. Ruman Gerst; Anna Medyukhina; Marc Thilo Figge: MISA++: A standardized interface for automated bioimage analysis (2020) not zbMATH
  4. Zheng, Xinye; Ye, Jianbo; Wang, James Z.; Li, Jia: Scott: shape-location combined tracking with optimal transport (2020)
  5. O’Mara, A., King, A.E., Vickers, J.C., Kirkcaldie, M.T.K.: ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests (2017) not zbMATH
  6. Serge Dmitrieff, François Nédélec: ConfocalGN: A minimalistic confocal image generator (2017) not zbMATH
  7. Śmieja, Marek; Wiercioch, Magdalena: Constrained clustering with a complex cluster structure (2017)
  8. Kröger, Thorben: Learning-based segmentation for connectomics (2014)
  9. Khan, Arif Ul Maula; Mikut, Ralf; Schweitzer, Brigitte; Weiss, Carsten; Reischl, Markus: Automatic tuning of image segmentation parameters by means of fuzzy feature evaluation (2013) ioport