Abstract
In this paper, we propose a novel approach for binary image reconstruction from few projections. The binary reconstruction problem can be highly underdetermined and one way to reduce the search space of feasible solutions is to exploit some prior knowledge of the image to be reconstructed. We use texture information extracted from sample image patches as prior knowledge. Experimental results show that this approach can retain the structure of the image even if just a very few number of projections are used for the reconstruction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahonen, T., Pietikäinen, M.: Soft histograms for local binary patterns. In: Proceedings of the Finnish Signal Processing Symposium, FINSIG, vol. 5, p. 1 (2007)
Batenburg, K.J., Sijbers, J.: DART: a practical reconstruction algorithm for discrete tomography. IEEE Trans. Image Process. 20(9), 2542–2553 (2011)
Brunetti, S., Del Lungo, A., Del Ristoro, F., Kuba, A., Nivat, M.: Reconstruction of 4-and 8-connected convex discrete sets from row and column projections. Linear Algebra Appl. 339(1–3), 37–57 (2001)
Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Springer, London (2009). https://doi.org/10.1007/978-1-84628-723-7
Herman, G.T., Kuba, A.: Discrete Tomography: Foundations, Algorithms, and Applications. Springer, New York (1999). https://doi.org/10.1007/978-1-4612-1568-4
Herman, G.T., Kuba, A.: Advances in Discrete Tomography and its Applications. Springer, New York (2008). https://doi.org/10.1007/978-0-8176-4543-4
Iakovidis, D.K., Keramidas, E.G., Maroulis, D.: Fuzzy local binary patterns for ultrasound texture characterization. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 750–759. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69812-8_74
Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, New York (1988)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Kuba, A., Herman, G.T., Matej, S., Todd-Pokropek, A.: Medical applications of discrete tomography. DIMACS Ser. Discret. Math. Theor. Comput. Sci. 55, 195–208 (2000)
Kylberg, G.: FLBP and SLBP implementations for Matlab. http://www.cb.uu.se/~gustaf/textureDescriptors/
Kylberg, G., Sintorn, I.M.: Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J. Image Video Process. 2013(1), 17 (2013)
Lukić, T., Balázs, P.: Binary tomography reconstruction based on shape orientation. Pattern Recognit. Lett. 79, 18–24 (2016)
Lukić, T., Lukity, A., Gogolák, L.: Binary tomography reconstruction method with perimeter preserving regularization. In: Proceedings of the 8th Conference of the Hungarian Association for Image Processing and Pattern Recognition, pp. 83–91 (2011)
Mehta, R., Egiazarian, K.: Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recognit. Lett. 71, 16–22 (2016)
Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634–3641 (2012)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Ryser, H.: Combinatorial properties of matrices of zeros and ones. Canad. J. Math. 9, 371–377 (1957)
Szűcs, J., Balázs, P.: Binary image reconstruction using local binary pattern priors. Int. J. Circuits Syst. Signal Process. 11, 296–299 (2017)
Waller, B., Nixon, M., Carter, J.: Image reconstruction from local binary patterns. In: International Conference on Signal-Image Technology and Internet-Based Systems, pp. 118–123 (2013)
Zhao, G.: LBP implementation for MATLAB. http://www.cse.oulu.fi/CMV/Downloads/LBPMatlab
Acknowledgements
Judit Szűcs was supported by the UNKP-18-3 New National Excellence Program of the Ministry of Human Capacities. This research was supported by the project “Integrated program for training new generation of scientists in the fields of computer science”, no. EFOP-3.6.3-VEKOP-16-2017-00002. The project has been supported by the European Union and co-funded by the European Social Fund. The authors thank Péter Bodnár for helping in implementation issues.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Szűcs, J., Balázs, P. (2019). Binary Tomography Using Variants of Local Binary Patterns as Texture Priors. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-29888-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29887-6
Online ISBN: 978-3-030-29888-3
eBook Packages: Computer ScienceComputer Science (R0)