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AI-based Feature Detection in X-ray-CT Images Using Synthesized Data

Published online by Cambridge University Press:  30 July 2020

Matthew Konnik ,
Bahar Ahmadi ,
Nicholas May ,
Joseph Favata ,
Zahra Shahbazi ,
Sina Shahbazmohamadi  and
Pouya Tavousi
Matthew Konnik
Affiliation:
University of Illinois Champaign-Urbana, Urbana, Illinois, United States
Bahar Ahmadi
Affiliation:
UCONN/REFINE lab, Vernon Rockville, Connecticut, United States
Nicholas May
Affiliation:
REFINE lab/ UCONN, Storrs, Connecticut, United States
Joseph Favata
Affiliation:
University of Connecticut, Storrs, Connecticut, United States
Zahra Shahbazi
Affiliation:
Manhattan College, Riverdale, New York, United States
Sina Shahbazmohamadi
Affiliation:
University of Connecticut, Storrs, Connecticut, United States
Pouya Tavousi
Affiliation:
University of Connecticut, Storrs, Connecticut, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 2116 - 2120
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Copyright
Copyright © Microscopy Society of America 2020

References

Ingman, J. M., Jormanainen, J. P. A., Vulli, A. M., Ingman, J. D., Maula, K., Kärkkäinen, T. J. and Silventoinen, P., “Localization of dielectric breakdown defects in multilayer ceramic capacitors using 3D X-ray imaging,” Journal of the European Ceramic Society, vol. 39, pp. 1178–1185, 2019.10.1016/j.jeurceramsoc.2018.10.030CrossRefGoogle Scholar
Wang, G., Yu, H. and De Man, B., “An outlook on x-ray CT research and development,” Medical physics, vol. 35, pp. 1051–1064, 2008.10.1118/1.2836950CrossRefGoogle ScholarPubMed
Wei, Y., Yu, H., Hsieh, J., Cong, W. and Wang, G., “Scheme of computed tomography,” Journal of X-Ray Science and Technology, vol. 15, pp. 235–270, 2007.Google Scholar
Opitz, D. and Blundell, S., “Object recognition and image segmentation: the Feature Analyst® approach,” in Object-based image analysis, Springer, 2008, pp. 153-167.10.1007/978-3-540-77058-9_8CrossRefGoogle Scholar
Blaschke, T. and Lang, S., “Object based image analysis for automated information extraction--a synthesis,” in Measuring the Earth II ASPRS Fall Conference, 2006.Google Scholar
Gonzalez, R. C., Woods, R. E. and Eddins, S. L., Digital image processing using MATLAB, Pearson Education India, 2004.Google Scholar
Zaitoun, N. M. and Aqel, M. J., “Survey on image segmentation techniques,” Procedia Computer Science, vol. 65, pp. 797–806, 2015.10.1016/j.procs.2015.09.027CrossRefGoogle Scholar
Pal, N. R. and Pal, S. K., “A review on image segmentation techniques,” Pattern recognition, vol. 26, pp. 1277–1294, 1993.10.1016/0031-3203(93)90135-JCrossRefGoogle Scholar
Burger, W. and Burge, M. J., Principles of Digital Image Processing: Advanced Methods, Springer Science & Business Media, 2013.10.1007/978-1-84882-919-0CrossRefGoogle Scholar
Yuheng, S. and Hao, Y., “Image segmentation algorithms overview,” arXiv preprint arXiv:1707.02051, 2017.Google Scholar
Gao, H., Siu, W.-C. and Hou, C.-H., “Improved techniques for automatic image segmentation,” IEEE transactions on Circuits and Systems for Video Technology, vol. 11, pp. 1273–1280, 2001.10.1109/76.974681CrossRefGoogle Scholar
Wang, B., Gao, X., Tao, D. and Li, X., “A unified tensor level set for image segmentation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, pp. 857–867, 2009.10.1109/TSMCB.2009.2031090CrossRefGoogle ScholarPubMed