title: A bioimage informatics approach to automatically extract complex fungal networks.
abstract: BACKGROUND
  Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging.
  
  
  RESULTS
  We have developed and evaluated a high-throughput automated image analysis and processing approach using Phase Congruency Tensors and watershed segmentation to characterize complex fungal networks. The performance of the proposed approach is evaluated using complex images of saprotrophic fungal networks with 10(5)-10(6) edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks.
  
  
  METHODS
  The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software
  
  
  BACKGROUND
  boguslaw.obara@oerc.ox.ac.uk.
authors: Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk
authors: Boguslaw
authors: B
authors: Obara
authors: Vicente
authors: V
authors: Grau
authors: Mark D
authors: MD
authors: Fricker
mesh_terms: D000465:Algorithms; D005658:Fungi / Q000166:cytology*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D025282:Mycelium / Q000166:cytology*; D020075:Phanerochaete / Q000166:cytology