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 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. AVAILABILITY AND IMPLEMENTATION: The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software CONTACTS: [email protected].

Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images.