title: NET: a new framework for the vectorization and examination of network data.
abstract: BACKGROUND
  The analysis of complex networks both in general and in particular as pertaining to real biological systems has been the focus of intense scientific attention in the past and present. In this paper we introduce two tools that provide fast and efficient means for the processing and quantification of biological networks likeDrosophilatracheoles or leaf venation patterns: the Network Extraction Tool (NET) to extract data and the Graph-edit-GUI (GeGUI) to visualize and modify networks.
  
  
  RESULTS
  NET is especially designed for high-throughput semi-automated analysis of biological datasets containing digital images of networks. The framework starts with the segmentation of the image and then proceeds to vectorization using methodologies from optical character recognition. After a series of steps to clean and improve the quality of the extracted data the framework produces a graph in which the network is represented only by its nodes and neighborhood-relations. The final output contains information about the adjacency matrix of the graph, the width of the edges and the positions of the nodes in space.NETalso provides tools for statistical analysis of the network properties, such as the number of nodes or total network length. Other, more complex metrics can be calculated by importing the vectorized network to specialized network analysis packages.GeGUIis designed to facilitate manual correction of non-planar networks as these may contain artifacts or spurious junctions due to branches crossing each other. It is tailored for but not limited to the processing of networks from microscopy images ofDrosophilatracheoles.
  
  
  CONCLUSIONS
  The networks extracted byNETclosely approximate the network depicted in the original image.NETis fast, yields reproducible results and is able to capture the full geometry of the network, including curved branches. AdditionallyGeGUIallows easy handling and visualization of the networks.
authors.affiliation: Max Planck Institute for Dynamics and Self-Organization, Göttingen, Am Fassberg 17, Göttingen, 37077 Germany.
authors.forename: Jana
authors.identifier: 0000-0002-4274-4580
authors.initials: J
authors.lastname: Lasser
authors.affiliation: Department of Physics & Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, 19104-6396 PA USA.
authors.forename: Eleni
authors.initials: E
authors.lastname: Katifori
keywords: Data acquisition; Drosophila; Leaf venation; Network extraction; Software