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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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Small 3D stack of a collagen gelDimensions  collagen concentration 1.2 mg/ml. (A) Raw data, as recorded with confocal reflection microscopy, without any image processing. The lateral (x-, y-direction) resolution of the fibers is considerably better than the vertical (z-direction) resolution, due to the anisotropic point spread function. In addition, only fiber segments that run in small angles to the imaging plane are visible, due to the so-called blind spot effect. Moreover, the speckled appearance of the collagen network is an optical artifact of reflection microscopy; confocal images of fluorescently labeled collagen networks reveal continuous line networks [16]. (B) Corresponding reconstruction result, using the algorithm described in this paper. Note that voxels that appear to be missing in the reconstruction are located outside of the selected sub volume.
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pone-0036575-g001: Small 3D stack of a collagen gelDimensions collagen concentration 1.2 mg/ml. (A) Raw data, as recorded with confocal reflection microscopy, without any image processing. The lateral (x-, y-direction) resolution of the fibers is considerably better than the vertical (z-direction) resolution, due to the anisotropic point spread function. In addition, only fiber segments that run in small angles to the imaging plane are visible, due to the so-called blind spot effect. Moreover, the speckled appearance of the collagen network is an optical artifact of reflection microscopy; confocal images of fluorescently labeled collagen networks reveal continuous line networks [16]. (B) Corresponding reconstruction result, using the algorithm described in this paper. Note that voxels that appear to be missing in the reconstruction are located outside of the selected sub volume.

Mentions: Many biological materials, such as the cytoskeleton or the extracellular matrix, self-organize into complex networks by the polymerization of protein molecules into fibrils (Fig. 1). If the thickness of the fibrils is negligible compared to the pore size, the resulting structure can be mathematically described as a disordered line network. In general, the functional properties of these networks, such as their mechanical stiffness on the macroscopic scale, or their permeability for diffusing particles and for actively migrating cells on a microscopic scale, depend on the geometrical details of the microscopic network structure. In order to study the relationship between structure and function, it is therefore important to extract, or reconstruct, the 3D network structure from image stacks. One aspect of the reconstruction is the binarization of the intensity values of the image stack, so that each voxel is assigned one of two possible values, corresponding either to the solid phase (1, collagen fibers) or the liquid phase (0, surrounding medium). Another aspect of the reconstruction is the skeletonization, so that the optically broadened fibers are reduced to their central (medial) axis, with a width of only one 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)

Small 3D stack of a collagen gelDimensions  collagen concentration 1.2 mg/ml. (A) Raw data, as recorded with confocal reflection microscopy, without any image processing. The lateral (x-, y-direction) resolution of the fibers is considerably better than the vertical (z-direction) resolution, due to the anisotropic point spread function. In addition, only fiber segments that run in small angles to the imaging plane are visible, due to the so-called blind spot effect. Moreover, the speckled appearance of the collagen network is an optical artifact of reflection microscopy; confocal images of fluorescently labeled collagen networks reveal continuous line networks [16]. (B) Corresponding reconstruction result, using the algorithm described in this paper. Note that voxels that appear to be missing in the reconstruction are located outside of the selected sub volume.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3351466&req=5

pone-0036575-g001: Small 3D stack of a collagen gelDimensions collagen concentration 1.2 mg/ml. (A) Raw data, as recorded with confocal reflection microscopy, without any image processing. The lateral (x-, y-direction) resolution of the fibers is considerably better than the vertical (z-direction) resolution, due to the anisotropic point spread function. In addition, only fiber segments that run in small angles to the imaging plane are visible, due to the so-called blind spot effect. Moreover, the speckled appearance of the collagen network is an optical artifact of reflection microscopy; confocal images of fluorescently labeled collagen networks reveal continuous line networks [16]. (B) Corresponding reconstruction result, using the algorithm described in this paper. Note that voxels that appear to be missing in the reconstruction are located outside of the selected sub volume.
Mentions: Many biological materials, such as the cytoskeleton or the extracellular matrix, self-organize into complex networks by the polymerization of protein molecules into fibrils (Fig. 1). If the thickness of the fibrils is negligible compared to the pore size, the resulting structure can be mathematically described as a disordered line network. In general, the functional properties of these networks, such as their mechanical stiffness on the macroscopic scale, or their permeability for diffusing particles and for actively migrating cells on a microscopic scale, depend on the geometrical details of the microscopic network structure. In order to study the relationship between structure and function, it is therefore important to extract, or reconstruct, the 3D network structure from image stacks. One aspect of the reconstruction is the binarization of the intensity values of the image stack, so that each voxel is assigned one of two possible values, corresponding either to the solid phase (1, collagen fibers) or the liquid phase (0, surrounding medium). Another aspect of the reconstruction is the skeletonization, so that the optically broadened fibers are reduced to their central (medial) axis, with a width of only one 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
Related in: MedlinePlus