General method for classification of fiber families in fiber-reinforced materials: application to in-vivo human skin images

Abstract

Fiber structures play a major role for the function of fiber-reinforced materials such as biological tissue. An objective classification of the fiber orientations into fiber families is crucial to understand its mechanical properties. We introduce the Fiber Image Network Evaluation Algorithm (FINE algorithm) to classify and quantify the number of fiber families in scientific images. Each fiber family is characterized by an amplitude, a mean orientation, and a dispersion. A new alignment index giving the averaged fraction of aligned fibers is defined. The FINE algorithm is validated by realistic grayscale Monte-Carlo fiber images. We apply the algorithm to an in-vivo depth scan of second harmonic generation images of dermal collagen in human skin. The derived alignment index exhibits a crossover at a critical depth where two fiber families with a perpendicular orientation around the main tension line arise. This strongly suggests the presence of a transition from the papillary to the reticular dermis. Hence, the FINE algorithm provides a valuable tool for a reliable classification and a meaningful interpretation of in-vivo collagen fiber networks and general fiber reinforced materials. Biological tissue such as articular cartilage 1 , myocardium 2 , aortic valve 3 , arterial walls 4 , and skin 5 exhibit a stress strain behavior that strongly depends on the collagen fiber distribution. Fiber reinforced materials are classified by the underlying fiber network which is characterized by its anisotropy and the fiber orientation 6-8. Upon stretching, tensile forces are applied to biological specimens and collagen fibers align in the stretching direction 9-14. The characterization of the collagen network is typically determined by quantities like the orientation index, mean fiber orientation, and the fiber dispersion. These parameters are obtained from the angular orientation distribution which is commonly modeled by a pi-periodic von-Mises function 15-21. However, this approach assumes that all fibers are part of a single fiber family. Gasser et al. introduced a mechanical model for arterial walls which assumes the existence of two opposing collagen fiber families, which are oriented around a main direction 4. Parameters for this model are achieved by modeling the fiber orientation distribution using two pi-periodic von-Mises functions 22. Skin is of major relevance as it represents the largest organ of the human body. It is subject to diverse environmental stress conditions and also large mechanical strains. Langer lines, also known as cleavage lines, are reported to indicate the main orientation of collagen fibers in skin 16. We introduce the Fiber Image Networks Evaluation algorithm (FINE algorithm), which is based on the cumulative orientation distribution (COD), to classify and quantify the fiber network by means of fiber families. The FINE algorithm uses an iterative approach to identify the number of fiber families and their angular properties. The variance of the COD that is obtained by the adaptive Fourier filt...

Glisson’s capsule structure and function is altered in cirrhotic patients irrespective of etiology

“…Approximately 20 fibers were measured in 3-8 images per sample. Orientation and alignment of collagen fibers were also assessed on SHG Z-stacks using a previously described method (19).…”

“…Collagen fibers in controls were organized in highly aligned sheets layered perpendicularly on top of each other creating a 3D cross hatch pattern that has also been described as a "two fiber family" organization (22). This was quantified using a previously described method (19) which assigns fibers to directional families, clearly showing that in most cases two individual directional families alternate in contributing to each layer of the collagen network (Supplemental Fig. 5).…”

Electrical and network properties of flexible silver-nanowire composite electrodes under mechanical strain

“…A completely isotropic distribution leads to a vanishing orientation index, while a full alignment of the fibers yields an orientation index of one. 23,29 Samples with high concentrations (80 µg cm −2 and 120 µg cm −2 ) show no remarkable changes in orientation, whereas for the 40 µg cm −2 sample a change in orientation index by a factor of 4.5 was observed. However, no emerging anisotropic fiber family could be found by the FINE algorithm (see ESI †).…”

“…These images were analyzed using an algorithm that determines the number of fiber families, their amplitudes, mean orientation and dispersion, based on the cumulative angular orientation distribution, the FINE algorithm. 23 The analysis clearly shows that the nanowire networks consist of one isotropic fiber family (see ESI SI 1 † for details). Upon stretching, changes in the overall network morphology are observed by the orientation index as shown in Fig.…”

“…In order to obtain quantitative information on the network structure change upon stretching, the Fiber Image Network Evaluation (FINE) algorithm was employed. 23,24 Based on the micrographs, the FINE algorithm determines the number of fiber families, their amplitudes, mean orientation and dispersion, based on the cumulative angular orientation distribution. Monte-Carlo simulations on the nanowire networks and the corresponding network resistance confirm that the elastic polymer matrix under strain exhibits forces acting on the nanowire junctions in agreement with Hooke's law.…”