Concrete is one of the most commonly used construction materials. A deeper insight into its mechanical properties, in particular cracking behaviour, can be gained from stress tests. Computed tomography captures the microstructure of building materials, including crack initiation and propagation in a fully three-dimensional manner. However, the complex microstructure of concrete renders crack segmentation a very challenging task. Both, the validation of segmentation methods and the training of machine learning approaches, are hindered by the lack of reliable ground truth segmentations for real data sets. To overcome this problem, a novel procedure for generating pairs of semi-synthetic images and ground truth was introduced by the authors in a previous study. Using this semi-synthetic data, Hessian-based percolation and 3d U-net were identified as the most promising of eight approaches for crack segmentation. Here, we discuss adaptions of the methods that allow for a handling of additional features observed in real computed tomography data of concrete, in particular local variations in crack thickness.
Engineering materials often feature lower dimensional and directed structures such as cracks, fibers, or closedfacets in foams. The characterization of such structures in 3D is of particular interest in various applications inmaterials science. In image processing, knowledge of the local structure orientation can be used for structureenhancement, directional filtering, segmentation, or separation of interacting structures. The idea of usingbanks of directed structuring elements or filters parameterized by a discrete subset of the orientation space isproven to be effective for these tasks in 2D. However, this class of methods is prohibitive in 3D due to the highcomputational burden of filtering on a sufficiently fine discretization of the unit sphere.This paper introduces a method for 3D pixel-wise orientation estimation and directional filtering inspiredby the idea of adaptive refinement in discretized settings. Furthermore, an operator for distinction betweenisotropic and anisotropic structures is defined based on our method. This operator utilizes orientationinformation to successfully preserve structures with one or two dominant dimensions. Finally, feasibilityand effectiveness of the method are demonstrated on 3D micro-computed tomography images in three usecases: detection of a misaligned region in a fiber-reinforced material, crack detection in concrete, and facetdetection in partially closed foams.
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