Limits...
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

Related in: MedlinePlus

Insensitivity of the algorithm to variations in the input data quality.The algorithm produces stable results over a wide range of photomultiplier gain (A) and laser outlet power (B). Note that the data in (A) and (B) correspond to two collagen gels that have been fabricated under identical conditions. The slight differences in the observed pore sizes reflect sample-to-sample fluctuations.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3351466&req=5

pone-0036575-g011: Insensitivity of the algorithm to variations in the input data quality.The algorithm produces stable results over a wide range of photomultiplier gain (A) and laser outlet power (B). Note that the data in (A) and (B) correspond to two collagen gels that have been fabricated under identical conditions. The slight differences in the observed pore sizes reflect sample-to-sample fluctuations.

Mentions: We performed two tests to evaluate the sensitivity of the algorithm towards variations in the image quality. First, the laser power was kept constant at 3 mW (wavelength 488 nm), while the gain of the photomultiplier tubes was changed over a range of 100 V, corresponding to an intensity variation by a factor of 3.6 (Fig. 11A). Second, the gain was kept constant at a value that gave optimal images for a laser power of 3 mW, while the laser power was varied from 0.6 mW to 5.5 mW, corresponding to an intensity variation by a factor of 5 (Fig. 11B). We find that our binarization algorithm is largely independent of the imaging parameters and the resulting differences in the image quality.


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)

Insensitivity of the algorithm to variations in the input data quality.The algorithm produces stable results over a wide range of photomultiplier gain (A) and laser outlet power (B). Note that the data in (A) and (B) correspond to two collagen gels that have been fabricated under identical conditions. The slight differences in the observed pore sizes reflect sample-to-sample fluctuations.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0036575-g011: Insensitivity of the algorithm to variations in the input data quality.The algorithm produces stable results over a wide range of photomultiplier gain (A) and laser outlet power (B). Note that the data in (A) and (B) correspond to two collagen gels that have been fabricated under identical conditions. The slight differences in the observed pore sizes reflect sample-to-sample fluctuations.
Mentions: We performed two tests to evaluate the sensitivity of the algorithm towards variations in the image quality. First, the laser power was kept constant at 3 mW (wavelength 488 nm), while the gain of the photomultiplier tubes was changed over a range of 100 V, corresponding to an intensity variation by a factor of 3.6 (Fig. 11A). Second, the gain was kept constant at a value that gave optimal images for a laser power of 3 mW, while the laser power was varied from 0.6 mW to 5.5 mW, corresponding to an intensity variation by a factor of 5 (Fig. 11B). We find that our binarization algorithm is largely independent of the imaging parameters and the resulting differences in the image quality.

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
Related in: MedlinePlus