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Enhancing CT 3D Images by Independent Component Analysis of Projection Images

  • Markus Hannula
  • Jari A. K. Hyttinen
  • Jarno M. A. TanskanenEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Computed tomography (CT) is an imaging modality producing 3D images from sets of 2D X-ray images taken around the object. The images are noisy by nature, and segmentation of the 3D images is tedious. Also, detection of low contrast objects may be difficult, if not impossible. Here, we propose an independent component analysis (ICA) based method to process sets of 2D projection images prior to 3D reconstruction to remove noise, and to enhance objects for detection and segmentation. In this paper, a proof-of-concept is provided: the proposed method was able to separate noise and image components, as well as to make visible objects that were not observable in 3D images without processing. We demonstrate our method in object separation with 2D slice image processing simulations, and by enhancing a 3D image of a polymer sample taken with Xradia MicroXCT-400. The method is applicable in any CT tomography for which a number of project image sets with different contrasts can be taken, e.g., in multispectral fashion.

Keywords

Computed tomography CT µCT Micro-CT Independent component analysis Image processing 3D imaging 

Notes

Acknowledgments

The work of J. M. A. Tanskanen has been supported by Jane and Aatos Erkko Foundation, Finland, under the project Biological Neuronal Communications and Computing with ICT. The work of M. Hannula has been supported by the Human Spare Parts Project funded by Finnish Funding Agency for Technology and Innovation (TEKES).

Conflict of Interest Declaration

No conflict of interest.

References

  1. 1.
    Cierniak, R.: X-ray Computed Tomography in Biomedical Engineering. Springer, London (2011).  https://doi.org/10.1007/978-0-85729-027-4CrossRefGoogle Scholar
  2. 2.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Hoboken (2001).  https://doi.org/10.1002/0471221317CrossRefGoogle Scholar
  3. 3.
    Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999).  https://doi.org/10.1109/72.761722CrossRefGoogle Scholar
  4. 4.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4–5), 411–430 (2000).  https://doi.org/10.1016/S0893-6080(00)00026-5CrossRefGoogle Scholar
  5. 5.
    Tang, S., Wang, Y., Chen, Y.-W.: Application of ICA to X-ray coronary digital subtraction angiography. Neurocomputing 79, 168–172 (2012).  https://doi.org/10.1016/j.neucom.2011.10.012CrossRefGoogle Scholar
  6. 6.
    Chen, Y.W., Han, X., Oikawa, S., Fujita, A.: Independent component analysis for removing X-ray scatter in X-ray images. In: Proceedings of the 2007 IEEE Instrumentation and Measurement Technology Conference, pp. 1–4. IEEE, Piscataway (2007).  https://doi.org/10.1109/IMTC.2007.379278
  7. 7.
    FastICA package for Matlab. http://www.cis.hut.fi/projects/ica/fastica/. Accessed 18 Apr 2019
  8. 8.
    Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002).  https://doi.org/10.1007/b98835CrossRefzbMATHGoogle Scholar
  9. 9.
    Tanskanen, J.M.A., Viik, J.J.: Independent component analysis in ECG signal processing. In: Millis, R.M. (ed.) Advances in Electrocardiograms, pp. 349–372. InTech, Rijeka (2012).  https://doi.org/10.5772/22719CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.BioMediTech, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland

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