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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.

Show MeSH
Voxel intensity distributions of the solid and the fluid phase.The two distributions show a wide overlap. No global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.
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pone-0036575-g003: Voxel intensity distributions of the solid and the fluid phase.The two distributions show a wide overlap. No global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.

Mentions: To perfectly reconstruct the original line network using global threshold binarization the existence of a threshold is required, such that all fluid phase voxels have a brightness below this threshold and all solid phase voxels have a brightness above this threshold. However, when we use our synthetic image stacks and plot the brightness distributions of the two phases separately, we find in general two peaks with a significant overlap (Fig. 3). This means that no global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.


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)

Voxel intensity distributions of the solid and the fluid phase.The two distributions show a wide overlap. No global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0036575-g003: Voxel intensity distributions of the solid and the fluid phase.The two distributions show a wide overlap. No global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.
Mentions: To perfectly reconstruct the original line network using global threshold binarization the existence of a threshold is required, such that all fluid phase voxels have a brightness below this threshold and all solid phase voxels have a brightness above this threshold. However, when we use our synthetic image stacks and plot the brightness distributions of the two phases separately, we find in general two peaks with a significant overlap (Fig. 3). This means that no global threshold can be found, even in principle, for separating the two phases, without also producing some false positive and false negative voxels.

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