Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distinguish a wide range of 3D organizations. In this article, we demonstrate an algorithm for quantitative classification of 3D collagen fiber organization. The algorithm first simulates five groups, or classifications, of fiber organization: unidirectional, crimped, disordered, two-fiber family, and helical. These five groups are widespread in natural tissues and are known to affect the tissue's mechanical properties. We use quantitative metrics based on features such as preferred 3D fiber orientation and spherical variance to differentiate each classification in a repeatable manner. We validate our algorithm by applying it to second-harmonic generation images of collagen fibers in tendon and cervix tissue that has been sectioned in specified orientations, and we find strong agreement between classification from simulated data and the physical fiber organization. Our approach provides insight for interpreting 3D fiber organization directly from volumetric images. This algorithm could be applied to other fiber-like structures that are not necessarily made of collagen.

We present a prop-based, tangible interface for 3D interactive visualization of thin fiber structures. These data are commonly found in current bioimaging datasets, for example second-harmonic generation microscopy of collagen fibers in tissue. Our approach uses commodity visualization technologies such as a depth sensing camera and low-cost 3D display. Unlike most current uses of these emerging technologies in the games and graphics communities, we employ the depth sensing camera to create a fish-tank stereoscopic virtual reality system at the scientist's desk that supports tracking of small-scale gestures with objects already found in the work space. We apply the new interface to the problem of interactive exploratory visualization of three-dimensional thin fiber data. A critical task for the visual analysis of these data is understanding patterns in fiber orientation throughout a volume.The interface enables a new, fluid style of data exploration and fiber orientation analysis by using props to provide needed passive-haptic feedback, making 3D interactions with these fiber structures more controlled. We also contribute a low-level algorithm for extracting fiber centerlines from volumetric imaging. The system was designed and evaluated with two biophotonic experts who currently use it in their lab. As compared to typical practice within their field, the new visualization system provides a more effective way to examine and understand the 3D bioimaging datasets they collect.