The aim of this work is to extract the architecture of biopolymer networks from 3D images. This is motivated to further understand non-affine regimes, found during network formation and in low-density biopolymer networks, where the geometry of a network has a fundamental role in defining its mechanical properties. Firstly, developed image analysis tools were extended to 3D and contributed to high-performance open-source libraries for image analysis. These developments in isotropic wavelets will help in extracting realistic networks by removing spurious noise generated during image acquisition. Secondly, images of biopolymer gels from transmission electron microscopy (TEM), were used to reliably extract the network architecture. The imaged material was also studied with small-angle x-ray scattering (SAXS), and the comparison showed a strong agreement for
network-size features. Thirdly, spatial graphs were extracted from the image. A one-to-one map is provided between image and graph, keeping all the geometric information from the image. This then opened the door to using analytical tools from the complex networks field to characterize images. Finally, statistical distributions extracted from three graph properties were used to reconstruct a completely in-silico network using a simulated annealing technique to generate new networks. This can be used as a computational exploration tool of how network behavior depends on network architecture.