Systems Approach to Human Hair Fibers: Interdependence Between Physical, Mechanical, Biochemical and Geometric Properties of Natural Healthy Hair

Contextual interpretation of hair fiber data is often blind to the effects of the dynamic complexity between different fiber properties. This intrinsic complexity requires systems thinking to decipher hair fiber accurately. Hair research, studied by various disciplines, follows a reductionist research approach, where elements of interest are studied from a local context with a certain amount of detachment from other elements or contexts. Following a systems approach, the authors are currently developing a cross-disciplinary taxonomy to provide a holistic view of fiber constituents and their interactions within large-scale dynamics. Based on the development process, this paper presents a review that explores the associated features, interrelationships and interactive complexities between physical, mechanical, biochemical and geometric features of natural, healthy hair fibers. Through the review, the importance of an appropriate taxonomy for interpreting hair fiber data across different disciplines is revealed. The review also demonstrates how seemingly unrelated fiber constituents are indeed interdependent and that these interdependencies may affect the behavior of the fiber. Finally, the review highlights how a non-integrative approach may have a negative impact on the reliability of hair data interpretation.

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.