@misc{PaetschBaumEbelletal., author = {Paetsch, Olaf and Baum, Daniel and Ebell, Gino and Ehrig, Karsten and Heyn, Andreas and Meinel, Dietmar and Prohaska, Steffen}, title = {Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-50912}, abstract = {Kurzfassung. Durch die Alkalit{\"a}t des Betons wird Betonstahl dauerhaft vor Korrosion gesch{\"u}tzt. Infolge von Chlorideintrag kann dieser Schutz nicht l{\"a}nger aufrechterhalten werden und f{\"u}hrt zu Lochkorrosion. Die zerst{\"o}rungsfreie Pr{\"u}fung von Stahlbetonproben mit 3D-CT bietet die M{\"o}glichkeit, eine Probe mehrfach gezielt vorzusch{\"a}digen und den Korrosionsfortschritt zu untersuchen. Zur Quantifizierung des Sch{\"a}digungsgrades m{\"u}ssen die bei dieser Untersuchung anfallenden großen Bilddaten mit Bildverarbeitungsmethoden ausgewertet werden. Ein wesentlicher Schritt dabei ist die Segmentierung der Bilddaten, bei der zwischen Korrosionsprodukt (Rost), Betonstahl (BSt), Beton, Rissen, Poren und Umgebung unterschieden werden muss. Diese Segmentierung bildet die Grundlage f{\"u}r statistische Untersuchungen des Sch{\"a}digungsfortschritts. Hierbei sind die {\"A}nderung der BSt-Geometrie, die Zunahme von Korrosionsprodukten und deren Ver{\"a}nderung {\"u}ber die Zeit sowie ihrer r{\"a}umlichen Verteilung in der Probe von Interesse. Aufgrund der Gr{\"o}ße der CT-Bilddaten ist eine manuelle Segmentierung nicht durchf{\"u}hrbar, so dass automatische Verfahren unabdingbar sind. Dabei ist insbesondere die Segmentierung der Korrosionsprodukte in den Bilddaten ein schwieriges Problem. Allein aufgrund der Grauwerte ist eine Zuordnung nahezu unm{\"o}glich, denn die Grauwerte von Beton und Korrosionsprodukt unterscheiden sich kaum. Eine formbasierte Suche ist nicht offensichtlich, da die Korrosionsprodukte in Beton diffuse Formen haben. Allerdings l{\"a}sst sich Vorwissen {\"u}ber die Ausbreitung der Korrosionsprodukte nutzen. Sie bilden sich in r{\"a}umlicher N{\"a}he des BSt (in Bereichen vorheriger Volumenabnahme des BSt), entlang von Rissen sowie in Porenr{\"a}umen, die direkt am BSt und in dessen Nahbereich liegen. Davon ausgehend wird vor der Korrosionsprodukterkennung zun{\"a}chst eine BSt-Volumen-, Riss- und Porenerkennung durchgef{\"u}hrt. Dieser in der Arbeit n{\"a}her beschriebene Schritt erlaubt es, halbautomatisch Startpunkte (Seed Points) f{\"u}r die Korrosionsprodukterkennung zu finden. Weiterhin werden verschiedene in der Bildverarbeitung bekannte Algorithmen auf ihre Eignung untersucht werden.}, language = {de} } @inproceedings{PaetschBaumProhaskaetal., author = {Paetsch, Olaf and Baum, Daniel and Prohaska, Steffen and Ehrig, Karsten and Meinel, Dietmar and Ebell, Gino}, title = {3D Corrosion Detection in Time-dependent CT Images of Concrete}, series = {DIR-2015 Proceedings}, booktitle = {DIR-2015 Proceedings}, abstract = {In civil engineering, the corrosion of steel reinforcements in structural elements of concrete bares a risk of stability-reduction, mainly caused by the exposure to chlorides. 3D computed tomography (CT) reveals the inner structure of concrete and allows one to investigate the corrosion with non-destructive testing methods. To carry out such investigations, specimens with a large artificial crack and an embedded steel rebar have been manufactured. 3D CT images of those specimens were acquired in the original state. Subsequently three cycles of electrochemical pre-damaging together with CT imaging were applied. These time series have been evaluated by means of image processing algorithms to segment and quantify the corrosion products. Visualization of the results supports the understanding of how corrosion propagates into cracks and pores. Furthermore, pitting of structural elements can be seen without dismantling. In this work, several image processing and visualization techniques are presented that have turned out to be particularly effective for the visualization and segmentation of corrosion products. Their combination to a workflow for corrosion analysis is the main contribution of this work.}, language = {en} } @article{PaetschBaumProhaskaetal., author = {Paetsch, Olaf and Baum, Daniel and Prohaska, Steffen and Ehrig, Karsten and Ebell, Gino and Meinel, Dietmar and Heyn, Andreas}, title = {Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben}, series = {DGZfP-Jahrestagung 2014 Konferenzband}, journal = {DGZfP-Jahrestagung 2014 Konferenzband}, language = {de} } @inproceedings{EhrigGoebbelsMeineletal.2011, author = {Ehrig, Karsten and Goebbels, J{\"u}rgen and Meinel, Dietmar and Paetsch, Olaf and Prohaska, Steffen and Zobel, Valentin}, title = {Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography}, series = {International Symposium on Digital Industrial Radiology and Computed Tomography}, booktitle = {International Symposium on Digital Industrial Radiology and Computed Tomography}, year = {2011}, language = {en} } @inproceedings{PaetschBaumEhrigetal.2012, author = {Paetsch, Olaf and Baum, Daniel and Ehrig, Karsten and Meinel, Dietmar and Prohaska, Steffen}, title = {Vergleich automatischer 3D-Risserkennungsmethoden f{\"u}r die quantitative Analyse der Schadensentwicklung in Betonproben mit Computer-Tomographie}, series = {Tagungsband der DACH Jahrestagung 2012}, booktitle = {Tagungsband der DACH Jahrestagung 2012}, year = {2012}, language = {de} } @inproceedings{PaetschBaumBressleretal.2013, author = {Paetsch, Olaf and Baum, Daniel and Breßler, David and Ehrig, Karsten and Meinel, Dietmar and Prohaska, Steffen}, title = {3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben}, series = {Tagungsband der DGZfP Jahrestagung 2013}, booktitle = {Tagungsband der DGZfP Jahrestagung 2013}, year = {2013}, language = {de} } @inproceedings{PaetschBaumEhrigetal.2012, author = {Paetsch, Olaf and Baum, Daniel and Ehrig, Karsten and Meinel, Dietmar and Prohaska, Steffen}, title = {Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography}, series = {Proceedings of Conference on Industrial Computed Tomography}, booktitle = {Proceedings of Conference on Industrial Computed Tomography}, pages = {321 -- 330}, year = {2012}, language = {en} } @inproceedings{MeinelEhrigLhostisetal.2014, author = {Meinel, Dietmar and Ehrig, Karsten and Lhostis, Valrie and Muzeau, Benoist and Paetsch, Olaf}, title = {Examination of Damage Processes in Concrete with CT}, series = {Proceedings of Conference on Industrial Computed Tomography}, booktitle = {Proceedings of Conference on Industrial Computed Tomography}, year = {2014}, language = {en} } @article{TitschackBaumMatsuyamaetal.2018, author = {Titschack, J{\"u}rgen and Baum, Daniel and Matsuyama, Kei and Boos, Karin and F{\"a}rber, Claudia and Kahl, Wolf-Achim and Ehrig, Karsten and Meinel, Dietmar and Soriano, Carmen and Stock, Stuart R.}, title = {Ambient occlusion - a powerful algorithm to segment shell and skeletal intrapores in computed tomography data}, series = {Computers and Geosciences}, volume = {115}, journal = {Computers and Geosciences}, doi = {10.1016/j.cageo.2018.03.007}, pages = {75 -- 87}, year = {2018}, abstract = {During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance- map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.}, language = {en} } @misc{TitschackBaumMatsuyamaetal., author = {Titschack, J{\"u}rgen and Baum, Daniel and Matsuyama, Kei and Boos, Karin and F{\"a}rber, Claudia and Kahl, Wolf-Achim and Ehrig, Karsten and Meinel, Dietmar and Soriano, Carmen and Stock, Stuart R.}, title = {Ambient occlusion - a powerful algorithm to segment shell and skeletal intrapores in computed tomography data}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-67982}, abstract = {During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably. Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance- map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.}, language = {en} }