Search

This paper introduces a method for detecting endpoints of partially overlapping straight fibers in three-dimensional voxel image data. The novel approach directly determines fiber endpoints without the need for more expansive single-fiber segmentation. In the context of fiber-reinforced polymers, endpoint information is of practical significance as it can indicate potential damage in endless fiber systems, or can serve as input for estimating statistical fiber length distribution. We tackle this challenge by exploiting Gaussian curvature of the surface of the fibers. Fiber endpoints have high positive curvature, allowing one to distinguish them from the rest of a structure. Accuracy data of the proposed method are presented for various data sets. For simulated fiber systems with fiber volume fractions of less than 20 %, true positive rates above 94 % and false positive rates below 5 % are observed. Two well-resolved real data sets show a reduction of the first rate to 90.3 % and an increase of the second rate to 13.1 %.

This paper tackles the non-trivial image-processing task to segment hook-ended fibers in three-dimensional images. For this purpose, a novel segmentation method is presented that relies on the following observation: For a single fiber the configurations of principal curvatures that can occur on its surface are limited. Deviations from these configurations indicate potential overlaps of fibers. The method that was developed based on this observation is used to separate several simulated clusters of touching fibers as a proof-of-concept. Further, it is applied to two images of cracked steel fiber reinforced concrete specimens arising from a 4-point bending test. The method's performance is compared to manual separation. Overall, we can state that the proposed method yields satisfying results when data meets the following criteria: Low fiber volume density, circular fiber cross section and sufficient spatial resolution of fiber-fiber contacts.

Abstract Background There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. Results We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system’s effectiveness. Conclusion ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.

Natural-neighbor interpolation methods, such as Sibson's method, are well-known schemes for multivariate data fitting and reconstruction. Despite its many desirable properties, Sibson's method is computationally expensive and difficult to implement, especially when applied to higher-dimensional data. The main reason for both problems is the method's implementation based on a Voronoi diagram of all data points. We describe a discrete approach to evaluating Sibson's interpolant on a regular grid, based solely on finding nearest neighbors and rendering and blending d-dimensional spheres. Our approach does not require us to construct an explicit Voronoi diagram, is easily implemented using commodity three-dimensional graphics hardware, leads to a significant speed increase compared to traditional approaches, and generalizes easily to higher dimensions. For large scattered data sets, we achieve two-dimensional (2D) interpolation at interactive rates and 3D interpolation (3D) with computation times of a few seconds.