A micromechanically-based constitutive model for fibrous tissues is presented. The model considers the randomly crimped morphology of individual collagen fibers, a morphology typically seen in photomicrographs of tissue samples. It describes the relationship between the fiber endpoints and its arc-length in terms of a measurable quantity, which can be estimated from image data. The collective mechanical behavior of collagen fibers is presented in terms of an explicit expression for the strain-energy function, where a fiber-specific random variable is approximated by a Beta distribution. The model-related stress and elasticity tensors are provided. Two representative numerical examples are analyzed with the aim of demonstrating the peculiar mechanism of the constitutive model and quantifying the effect of parameter changes on the mechanical response. In particular, a fibrous tissue, assumed to be (nearly) incompressible, is subject to a uniaxial extension along the fiber direction, and, separately, to pure shear. It is shown that the fiber crimp model can reproduce several of the expected characteristics of fibrous tissues.

Fluorescence microscopy allows the acquisition of the spectroscopic properties of fluorescent reporter molecules at levels of resolution too small to be seen with the naked eye. The Indirect Immune Fluorescence Test is the method used to identify antinuclear antibodies. The main principle of this method is to identify the auto-antibodies in a patient's blood serum by staining affected cell structures. The resulting auto-antibody specific fluorescence patterns can be visualized by a fluorescence microscope and examined by a physician to determine a diagnosis. More than 30 different nuclear and cytoplasmic fluorescence patterns are known, which are characterized by a set of a 100 different auto-antibodies. The quality of a suspicion diagnosis strongly depends on the experience of the physicians and, as such, can be very subjective. This paper focuses on the development and evaluation of image processing and classification algorithms for HEp-2 Cell segmentation and cell type classification in order to better detect a suspicion diagnosis for auto-immune diseases.

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