Parameter-Free Binarization and Skeletonization of Fiber Networks from Confocal Image Stacks.

We present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from threedimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3x3x3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise. [ABSTRACT FROM AUTHOR]