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Parameter-free binarization and skeletonization of fiber networks from confocal image stacks.

Krauss P, Metzner C, Lange J, Lang N, Fabry B - PLoS ONE (2012)

Bottom Line: The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic.Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood.This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biophysics Group, Friedrich-Alexander University, Erlangen, Germany.

ABSTRACT
We present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

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Flow chart of the reconstruction algorithm.The method involves three independent 2D scans through the 3D image stack, along the x-, y- and z-directions. Since these scans are analogous, the diagram focuses on the x-scan only.
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pone-0036575-g005: Flow chart of the reconstruction algorithm.The method involves three independent 2D scans through the 3D image stack, along the x-, y- and z-directions. Since these scans are analogous, the diagram focuses on the x-scan only.

Mentions: The following section outlines the basic methods used in the process of network reconstruction (see also the flow chart in Fig. 5). A more detailed description of the algorithm is available as a preprint: arXiv:1111.3861. In addition, a C++ implementation of the algorithm and a sample data set are freely available at http://tiny.cc/2012-Krauss-PlosOne-Prog.


Parameter-free binarization and skeletonization of fiber networks from confocal image stacks.

Krauss P, Metzner C, Lange J, Lang N, Fabry B - PLoS ONE (2012)

Flow chart of the reconstruction algorithm.The method involves three independent 2D scans through the 3D image stack, along the x-, y- and z-directions. Since these scans are analogous, the diagram focuses on the x-scan only.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351466&req=5

pone-0036575-g005: Flow chart of the reconstruction algorithm.The method involves three independent 2D scans through the 3D image stack, along the x-, y- and z-directions. Since these scans are analogous, the diagram focuses on the x-scan only.
Mentions: The following section outlines the basic methods used in the process of network reconstruction (see also the flow chart in Fig. 5). A more detailed description of the algorithm is available as a preprint: arXiv:1111.3861. In addition, a C++ implementation of the algorithm and a sample data set are freely available at http://tiny.cc/2012-Krauss-PlosOne-Prog.

Bottom Line: The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic.Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood.This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biophysics Group, Friedrich-Alexander University, Erlangen, Germany.

ABSTRACT
We present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

Show MeSH