Abstract
This chapter highlights the application of image processing for automated analysis of images generated by reconstructive imaging techniques (CT, MRI). It is shown that, in addition to the pure image information, contextual knowledge about the imaged structures as well as knowledge about acquisition- or reconstruction-related artifacts is always necessary in order to obtain qualitatively good results. The consideration of this contextual information, however, is complicated in the classical way and is rarely directly transferable to modified questions. The application of deep neural networks to such questions offers a great potential. This is explained, demonstrated, and discussed using the semantic segmentation of image data.
This is a preview of subscription content, log in via an institution to check access.
References
Schneider CA, Rasband WS, Eliceiri KW. NIH image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–5. PMID 22930834.
Creuz A, 3D-dimensionelles Messen im μm-Bereich: Möglichkeiten und Grenzen der Nanofokus-Tomographie zur korrekten Geometrieermittlung von Dieselinjektoren. Bachelor thesis, Beuth University for Applied Sciences; 2015.
Hubel D, Wiesel T. Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. J Physiol. 1962;160:106–54.
LeCun Y, Haffner P, Bottou L, Bengio Y. Object recognition with gradient-based learning. In: Shape, contour and grouping in computer vision. Berlin/Heidelberg: Springer; 1999. p. 319–45.
Badrinarayanan V, Handa A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labeling. CoRR; 2015.
Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–95.
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning, vol. 37; 2015. p. 448–56.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention (MICCAI), vol. 9351. Springer; 2015. p. 234–41.
Chu C, Belavy DL, Armbrecht G, Bansmann M, Felsenberg D, Zheng G. Annotated T2-weighted MR images of the lower spine. Zenodo; 2015.
Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning, arXiv; 2018.
Jegou S, Vazquez D, Romero A, Bengio Y. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW); 2017. p. 1175–83
Cicek O, Abdulkdair A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention (MICCAI). Springer International Publishing; 2016. p. 424–32.
Zhao W, Jiang D, Queralta J, Westerlund T. MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net. Inform Med Unlocked. 2020;19:100357.
Fang Q, Ibarra-Castanedo C, Maldague X. Automatic defects segmentation and identification by deep learning algorithm with pulsed thermography: synthetic and experimental data. Big Data Cogn Comput. 2021;5(1):9.
He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, Venice; 2017. p. 2961–9
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV); 2017. p. 618–26.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this entry
Cite this entry
Tewes, A.H.J., Haibel, A., Schneider, R.P. (2021). Image Processing 2D/3D with Emphasis on Image Segmentation. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-48200-8_59-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-48200-8_59-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48200-8
Online ISBN: 978-3-030-48200-8
eBook Packages: Springer Reference Chemistry and Mat. ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics