A thorough understanding and analysis of geometry and topology of three-dimensional fiber networks from high resolution images is an important and challenging task due to the enormous complexity and randomness of the structure. In this paper we propose an image analysis system that is aimed at structural analysis of fibrous materials using their three dimensional images obtained from X-ray computed microtomography.; The raw images are cleaned and segmented using two segmentation methods; diffusion based and kriging based segmentations. The digitized fibers are then thinned to their skeletons, producing the medial axis of fibers which contain the rich geometric and topological information of the original object, by medial axis transform. A description of the network is then determined from the medial axis.; In analyzing a fiber network, individual fiber identification is the most crucial task. We demonstrate computational algorithms that can efficiently identify individual fibers from a network of randomly oriented and arbitrarily curled fibers that are touching and crossing irregularly with each other. This requires tracing fibers through the crossings and pairing appropriate free fiber segments at each crossing. We can accurately measure the orientation, location, curl, length, bonds, and crossing angles of the identified fibers in the 3D space as well as the porosity of the material contained in a given imaged volume. The performance of the proposed technique is tested on three simulated fiber data sets and the results are presented for a nonwoven polymer fiber mat and for a natural cellulosic fiber mat.
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