title: img2net: automated network-based analysis of imaged phenotypes.
abstract: CONCLUSIONS
  Automated analysis of imaged phenotypes enables fast and reproducible quantification of biologically relevant features. Despite recent developments, recordings of complex networked structures, such as leaf venation patterns, cytoskeletal structures or traffic networks, remain challenging to analyze. Here we illustrate the applicability of img2net to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties and statistically comparing networks of different types or under different conditions. The software can be readily used for analyzing image data of arbitrary 2D and 3D network-like structures.
  
  
  METHODS
  img2net is open-source software under the GPL and can be downloaded from http://mathbiol.mpimp-golm.mpg.de/img2net/, where supplementary information and datasets for testing are provided.
  
  
  BACKGROUND
  breuer@mpimp-golm.mpg.de.
authors: Mathematical Modeling and Systems Biology, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.
authors: David
authors: D
authors: Breuer
authors: Mathematical Modeling and Systems Biology, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.
authors: Zoran
authors: Z
authors: Nikoloski
mesh_terms: D008841:Actin Cytoskeleton / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010641:Phenotype; D012984:Software