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A bioimage informatics approach to automatically extract complex fungal networks

Lookup NU author(s): Professor Boguslaw ObaraORCiD


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Motivation: 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 105-106 edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks. © The Author 2012. Published by Oxford University Press. All rights reserved.

Publication metadata

Author(s): Obara B, Grau V, Fricker MD

Publication type: Article

Publication status: Published

Journal: Bioinformatics

Year: 2012

Volume: 28

Issue: 18

Pages: 2374-2381

Print publication date: 15/09/2012

Online publication date: 27/06/2012

ISSN (print): 1367-4803

ISSN (electronic): 1460-2059

Publisher: Oxford University Press


DOI: 10.1093/bioinformatics/bts364

PubMed id: 22743223


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