Microstructural analysis of a C/SiC ceramic based on the segmentation of X-ray phase contrast tomographic data

Wirjadi, Oliver; Rack, Alexander; Liebscher, André; Meinhardt, Jürgen; Schladitz, Katja; Shafei, Behrang; Steidl, Gabriele

doi:10.3139/146.111071

Cited by 4 publications

(4 citation statements)

References 30 publications

(30 reference statements)

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“…The Frangi filter was originally developed to detect tube-like structures in medical images [27] and can be adjusted to also detect plate-like structures [29]. Similar to the sheet filter, it is based on a comparison of the eigenvalues of the Hessian matrix.…”

Section: Sheet Filter (Sf)mentioning

confidence: 99%

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

Barisin¹,

Jung²,

Müsebeck³

et al. 2021

Preprint

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Concrete is the standard construction material for buildings, bridges, and roads.As safety plays a central role in the design, monitoring, and maintenance of such constructions, it is important to understand the cracking behavior of concrete. Computed tomography captures the microstructure of building materials and allows to study crack initiation and propagation. Manual segmentation of crack surfaces in large 3d images is not feasible. In this paper, automatic crack segmentation methods for 3d images are reviewed and compared. Classical image processing methods (edge detection filters, template matching, minimal path and region growing algorithms) and learning methods (convolutional neural networks, random forests) are considered and tested on semi-synthetic 3d images. Their performance strongly depends on parameter selection which should be adapted to the grayvalue distribution of the images and the geometric properties of the concrete. In general, the learning methods perform best, in particular for thin cracks and low grayvalue contrast.

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Section: Sheet Filter (Sf)mentioning

confidence: 99%

“…A typical task is the detection of tube-like structures such as blood vessels [27]. With the sheet and Frangi filters, these methods are adapted to planar structures [28,29], making them suitable for the segmentation of cracks. Supervised learning methods can be extended to 3d image data as well.…”

Section: Introductionmentioning

confidence: 99%

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

Barisin¹,

Jung²,

Müsebeck³

et al. 2021

Preprint

Self Cite

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show abstract

“… 7 Approaches based on filters such as sheet filters and Frangi filters performed well in image identification. 8 , 9 However, these approaches based on filters may miss some cracks when doing detection, and it would be better to consider template matching if thin cracks are important. 10 Other methods such as minimal path, and Hessian-based percolation were also used to identify images.…”

Section: Introductionmentioning

confidence: 99%

Crack detection for concrete bridges with imaged based deep learning

et al. 2022

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Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.

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“…In particular, the process of fast data reconstruction and the use of dedicated algorithms allow for the extraction of quantitative, e. g., statistical, information from images and hence, to push the utility of imaging results further towards model validation and virtual materials design [12]. Here, Ohser et al [13] introduce a new concept to analyze images of open foams in a quantitative manner, Wirjadi et al combine microtomograpy with the high sensitivity of X-ray phase contrast to analyse orientations and cracks in such low-contrast samples as fibre-reinforced SiC ceramics and fibre-reinforced polymers [14], [15]. Stalder et al combine microtomography with image analysis to overcome limitations of classical histological sectioning to understand bone regeneration [16].…”

mentioning

confidence: 99%