Machine Vision and Applications

, Volume 27, Issue 5, pp 647–661 | Cite as

A framework for the extraction of quantitative traits from 2D images of mature Arabidopsis thaliana

  • Marco AugustinEmail author
  • Yll HaxhimusaEmail author
  • Wolfgang Busch
  • Walter G. Kropatsch
Special Issue Paper


In this work, we propose an image-based phenotyping framework for the determination of quantitative traits from mature Arabidopsis thaliana plants. Two-dimensional (2D) images taken from the dried and flattened plants are analyzed regarding their geometry as well as their branching topology. The realistic branching architecture is hereby reconstructed from a single 2D image using a tracing approach with a semi-circular search window. The centerline segments of the tracing procedure are subsequently merged and labeled based on a hierarchical approach combining continuity properties with geometrical and topological information determined during tracing. This paper covers a detailed description of the proposed plant phenotyping pipeline from the image acquisition process until the extraction of the quantitative traits. The framework is evaluated using a set of 106 images and compared to a manual phenotyping approach as well as a semi-automatic image-based approach. The most relevant results of this evaluation are presented.


Image-based phenotyping Geometrical traits Topological traits Tracing Hierarchical reconstruction  Network of curvilinear structures 



We want to thank Svante Holm (Mid Sweden University, SE) and Alison Anastasio (University of Chicago, US) for planting and harvesting the plants, Man Yu and Andrew Davis for taking the photos and Benjamin Brachi (Bergelson Lab, University of Chicago, US) for his valuable inputs and support along the stages of development.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
  2. 2.Center for Medical Statistics, Informatics and Intelligent SystemsMedical University of ViennaViennaAustria
  3. 3.Faculty of Electrical and Computer EngineeringUniversity of PrishtinaPrishtinaKosovo
  4. 4.Gregor Mendel Institute of Molecular Plant BiologyAustrian Academy of SciencesViennaAustria
  5. 5.Pattern Recognition and Image Processing GroupVienna University of TechnologyViennaAustria

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