Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010, Article ID 545030, 9 pages doi:10.1155/2010/545030 Research Article Automatic Determination of Fiber-Length Distribution in Composite Material Using 3D CT Data Matthias Teßmann, 1 Stephan Mohr, 2 Svitlana Gayetskyy, 2 Ulf Haßler, 2 Randolf Hanke, 2 and G¨ unther Greiner 1 1 Computer Graphics Group, University of Erlangen-Nuremberg, Am Wolfsmantel 33, 91058 Erlangen, Germany 2 Fraunhofer EZRT, Dr.-Mack-Str. 81, 90762 Fuerth, Germany Correspondence should be addressed to Matthias Teßmann, matthias.tessmann@informatik.uni-erlangen.de Received 31 December 2009; Accepted 24 March 2010 Academic Editor: Jo˜ ao Manuel R. S. Tavares Copyright © 2010 Matthias Teßmann et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Determining fiber length distribution in fiber reinforced polymer components is a crucial step in quality assurance, since fiber length has a strong influence on overall strength, stiffness, and stability of the material. The approximate fiber length distribution is usually determined early in the development process, as conventional methods require a destruction of the sample component. In this paper, a novel, automatic, and nondestructive approach for the determination of fiber length distribution in fiber reinforced polymers is presented. For this purpose, high-resolution computed tomography is used as imaging method together with subsequent image analysis for evaluation. The image analysis consists of an iterative process where single fibers are detected automatically in each iteration step after having applied image enhancement algorithms. Subsequently, a model-based approach is used together with a priori information in order to guide a fiber tracing and segmentation process. Thereby, the length of the segmented fibers can be calculated and a length distribution can be deduced. The performance and the robustness of the segmentation method is demonstrated by applying it to artificially generated test data and selected real components. 1. Introduction Fiber reinforced polymers (FRPs) are used increasingly in the aerospace and automotive industry, since those com- ponents facilitate the cost-effective building of lightweight but rigid components. One manufacturing method for the construction of fiber reinforced polymers is the long fiber reinforced thermoplastics (LFRP-D) process, where a matrix material, for example consisting of polypropylene and additives, is heated and mixed with fibers, for example carbon or glass fibers. This process happens directly, that is, without the usage of an intermediate semifinished part. Thereby, components can be manufactured that are capable of acting as supporting elements with respect to rigidity and stability. Due to these properties, many parts made of LFRP are already used in the automotive industry, for example for frontends, underbody casing, supporting elements, or parts of an engine compartment. The length of the fibers has a strong influence on the strength, stiffness, and impact resistance of the component [1]. From measurements, it is known that 95% of the maximal possible material stiffness is reached at a fiber length of 1 mm. However, the desirable length of fibers within a certain component is strongly dependent on the purpose of the product [2]. Moreover, since fibers can be damaged and divided by the production device during the creation process, there is not only one single fiber length but a distribution of the fiber lengths within a component. Furthermore, the opposite can happen if the cutting of the fibers by the cutting unit does not work well, which results in longer fibers than expected [3]. Because of this uncertainty of the actual fiber length within the component, it is important to determine the distribution of the fibers and their lengths after the production process in order to estimate the quality of the resulting product. In this paper, a novel method for the determination of the fiber length distribution is proposed using high resolution computed tomography as scanning method. Therefore, the 3D CT image is processed in order to segment the fibers. Subsequently, the length of each segmented fiber can be