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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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Voxelized representation of collagen fibers.The figure shows three adjacent original confocal images (left) and the corresponding reconstruction result (right). The broadened fiber is reduced to a wiggly, continuous line with a ‘diameter’ of one voxel.
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pone-0036575-g009: Voxelized representation of collagen fibers.The figure shows three adjacent original confocal images (left) and the corresponding reconstruction result (right). The broadened fiber is reduced to a wiggly, continuous line with a ‘diameter’ of one voxel.

Mentions: The template matching algorithm reduces the optically blurred images of fibers to contiguous voxel chains of binary value 1 (Fig. 1, see also animation at http://tiny.cc/2012-Krauss-PlosOne-Mov). Within the discrete binary output stack, a fiber that follows a smooth curve in continuous space can only be represented as a wiggling trace of voxels (Fig. 9). This voxelization artifact has no significant effect on the pore size distribution of the line network, however, the analysis of other quantities, such as the local curvature of the fibers, would require further post-processing steps in order to fit smooth space curves through the discrete voxel chains. Alternatively, our template matching algorithm could be extended to sub-voxel accuracy: Once the discrete pixel has been determined in which a fiber intersects a given plane, the detailed sub-pixel position of the fiber’s medial axis in the plane could be found in a second step. This could be achieved, for example, by computing the center of intensity for a small 3D environment around the central voxel.


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)

Voxelized representation of collagen fibers.The figure shows three adjacent original confocal images (left) and the corresponding reconstruction result (right). The broadened fiber is reduced to a wiggly, continuous line with a ‘diameter’ of one voxel.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0036575-g009: Voxelized representation of collagen fibers.The figure shows three adjacent original confocal images (left) and the corresponding reconstruction result (right). The broadened fiber is reduced to a wiggly, continuous line with a ‘diameter’ of one voxel.
Mentions: The template matching algorithm reduces the optically blurred images of fibers to contiguous voxel chains of binary value 1 (Fig. 1, see also animation at http://tiny.cc/2012-Krauss-PlosOne-Mov). Within the discrete binary output stack, a fiber that follows a smooth curve in continuous space can only be represented as a wiggling trace of voxels (Fig. 9). This voxelization artifact has no significant effect on the pore size distribution of the line network, however, the analysis of other quantities, such as the local curvature of the fibers, would require further post-processing steps in order to fit smooth space curves through the discrete voxel chains. Alternatively, our template matching algorithm could be extended to sub-voxel accuracy: Once the discrete pixel has been determined in which a fiber intersects a given plane, the detailed sub-pixel position of the fiber’s medial axis in the plane could be found in a second step. This could be achieved, for example, by computing the center of intensity for a small 3D environment around the central voxel.

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