KNIME for Open-Source Bioimage Analysis: A Tutorial

  • Christian DietzEmail author
  • Michael R. Berthold
Part of the Advances in Anatomy, Embryology and Cell Biology book series (ADVSANAT, volume 219)


The open analytics platform KNIME is a modular environment that enables easy visual assembly and interactive execution of workflows. KNIME is already widely used in various areas of research, for instance in cheminformatics or classical data analysis. In this tutorial the KNIME Image Processing Extension is introduced, which adds the capabilities to process and analyse huge amounts of images. In combination with other KNIME extensions, KNIME Image Processing opens up new possibilities for inter-domain analysis of image data in an understandable and reproducible way.


Zernike Moment Bioimage Analysis Meta Node Haralick Texture Feature Local Workspace 
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.


  1. Aligeti M, Behrens RT, Pocock GM, Schindelin J, Dietz C, Eliceiri KW, Swanson CM, Malim MH, Ahlquist P, Sherer NM (2014) Cooperativity among rev-associated nuclear export signals regulates HIV-1 gene expression and is a determinant of virus species tropism. J Virol 88(24):14,207–14,221. doi:10.1128/JVI.01897-14. Google Scholar
  2. Allan C, Burel JM, Moore J, Blackburn C, Linkert M, Loynton S, MacDonald D, Moore WJ, Neves C, Patterson A et al (2012) Omero: flexible, model-driven data management for experimental biology. Nat Methods 9(3):245–253CrossRefPubMedPubMedCentralGoogle Scholar
  3. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz information miner. Springer, BerlinGoogle Scholar
  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  5. Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath B, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B et al (2012) Biological imaging software tools. Nat Methods 9(7):697–710CrossRefPubMedPubMedCentralGoogle Scholar
  6. Gunkel M, Flottmann B, Heilemann M, Reymann J, Erfle H (2014) Integrated and correlative high-throughput and super-resolution microscopy. Histochem Cell Biol 141(6):597–603. doi:10.1007/s00418-014-1209-y. Google Scholar
  7. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern (6):610–621CrossRefGoogle Scholar
  8. Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, Ljosa V, Rueden C, Eliceiri KW, Carpenter AE (2011) Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software. Bioinformatics 27(8):1179–1180CrossRefPubMedPubMedCentralGoogle Scholar
  9. Khotanzad A, Hong YH (1990) Invariant image recognition by zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497CrossRefGoogle Scholar
  10. Linkert M, Rueden CT, Allan C, Burel JM, Moore W, Patterson A, Loranger B, Moore J, Neves C, MacDonald D et al (2010) Metadata matters: access to image data in the real world. J Cell Biol 189(5):777–782CrossRefPubMedPubMedCentralGoogle Scholar
  11. Ljosa V, Sokolnicki KL, Carpenter AE (2012) Annotated high-throughput microscopy image sets for validation. Nat Methods 9(7):637CrossRefPubMedPubMedCentralGoogle Scholar
  12. Lodermeyer V et al (2013) 90k, an interferon-stimulated gene product, reduces the infectivity of HIV-1. Retrovirology 10(1):11CrossRefGoogle Scholar
  13. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27Google Scholar
  14. Pietzsch T, Preibisch S, Tomančák P, Saalfeld S (2012) Imglib2 - generic image processing in java. Bioinformatics 28(22):3009–3011CrossRefPubMedPubMedCentralGoogle Scholar
  15. Royer LA et al (2015) ClearVolume: open-source live 3D visualization for light-sheet microscopy. Nat Methods 12(6):480–481CrossRefPubMedGoogle Scholar
  16. Saha AK, Kappes F, Mundade A, Deutzmann A, Rosmarin DM, Legendre M, Chatain N, Al-Obaidi Z, Adams BS, Ploegh HL, Ferrando-May E, Mor-Vaknin N, Markovitz DM (2013) Intercellular trafficking of the nuclear oncoprotein dek. Proc Natl Acad Sci 110(17):6847–6852. doi:10.1073/pnas.1220751110 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682CrossRefPubMedGoogle Scholar
  18. Schölkopf B, Alexander JS (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT, CambridgeGoogle Scholar
  19. Strauch M, Luedke A, Muench D, Laudes T, Galizia CG, Martinelli E, Lavra L, Paolesse R, Ulivieri A, Catini A, Capuano R, Di Natale C (2014) More than apples and oranges: detecting cancer with a fruit fly’s antennaGoogle Scholar
  20. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Bioinformatics and Information MiningUniversity of KonstanzKonstanzGermany

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