Open AccessModelling collagen fibre orientation in porcine skin based upon confocal laser scanning microscopy
Author(s) -
Jor Jessica W. Y.,
Nielsen Poul M. F.,
Nash Martyn P.,
Hunter Peter J.
Publication year - 2011
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2011.00471.x
Subject(s) - reticular dermis , confocal laser scanning microscopy , dermis , materials science , reticular connective tissue , microscopy , orientation (vector space) , confocal , biomedical engineering , confocal microscopy , biophysics , anatomy , optics , biology , mathematics , geometry , physics , medicine
Background: The mechanical properties of skin, and its ability to resist a wide range of deformations, are mainly determined by the collagen network within the dermis. Aims: In order to quantify the structure–function relationship of skin, quantitative data on collagen orientation are acquired in this study. Materials & Methods: Saggital cryosections from the abdominal region of young pigs were stained with picrosirus red for collagen detection and images were acquired by confocal laser scanning microscopy (CLSM). Spatial distributions of collagen orientation were determined using a structure–tensor approach. Orientation data were fitted to a mixture of two von Mises distributions. Results: It was observed that collagen is organised into large bundles in the reticular dermis that run obliquely between the epidermis to hypodermis along two predominant orientations. Discussion: This distinct lattice structure was apparent in all sections, regardless of the sectioning orientation. Based on our observations from CLSM images,we propose a conceptual model expressed in terms of a density distribution function to describe collagen orientation. Conclusion: We demonstrate that two parameters of this distribution (the mean and spread parameter) may be directly determined using CLSM image analysis. An important advantage of this approach is that model parameters can be estimated directly from observable microstructural features.