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Concrete is the standard construction material for buildings, bridges, androads. As safety plays a central role in the design, monitoring, andmaintenance of such constructions, it is important to understand the crackingbehavior of concrete. Computed tomography captures the microstructure ofbuilding materials and allows to study crack initiation and propagation. Manualsegmentation of crack surfaces in large 3d images is not feasible. In thispaper, automatic crack segmentation methods for 3d images are reviewed andcompared. Classical image processing methods (edge detection filters, templatematching, minimal path and region growing algorithms) and learning methods(convolutional neural networks, random forests) are considered and tested onsemi-synthetic 3d images. Their performance strongly depends on parameterselection which should be adapted to the grayvalue distribution of the imagesand the geometric properties of the concrete. In general, the learning methodsperform best, in particular for thin cracks and low grayvalue contrast.

Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images