Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm
DOI:
https://doi.org/10.47839/ijc.23.2.3543Keywords:
Filtered Back Projection, FBP, Computerized Tomography, Sinogram, Head PhantomAbstract
Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.
References
M. J. Willemink and P. B. Noël, “The evaluation of image reconstruction for CT-from filtered back projection to artificial intelligence,” Eur. Radiol., vol. 29, pp. 2185–2195, 2019. https://doi.org/10.1007/s00330-018-5810-7.
J. Hsieh, Computed Tomography: Principles, Design, Artifacts, and Recent Advances, 2nd ed, Wiley, 2009.
L. W. Goldman, “Principles of CT and CT Technology,” Journal of Nuclear Medicine Technology, vol. 35, pp. 115-128, 2007. https://doi.org/10.2967/jnmt.107.042978.
Z. Qiao, G. Redler, B. Epel, and H. J. Halpern, “Comparison of parabolic filtration methods for 3D filtered back projection in pulsed EPR imaging,” J. Magn. Reson., vol. 248, pp. 42–53, 2014. https://doi.org/10.1016/j.jmr.2014.08.010.
J. B. T. M. Roerdink, M. A. Westenberg, “Data-parallel tomographic reconstruction: A comparison of filtered backprojection and direct Fourier reconstruction,” Parallel Computing, vol. 24, pp. 2129-2142, 1998. https://doi.org/10.1016/S0167-8191(98)00095-7.
S. Basu, Y. O. Bresler, “(N/sup 2/log/sub 2/N) filtered backprojection reconstruction algorithm for tomography,” IEEE Trans. Image Process, vol. pp. 1760–1773, 2000. https://doi.org/10.1109/83.869187.
T. Pipatsrisawat, A. Gacic, F. Franchetti, M. Puschel and J. M. F. Moura, “Performance analysis of the filtered backprojection image reconstruction algorithms,” Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'05, Philadelphia, March 18-23, 2005, pp. 153-156, https://doi.org/10.1109/ICASSP.2005.1416263.
M. T. Al Hussani, M. H. Ali Al Hayani, “The use of filtered back projection algorithm for reconstruction of tomographic image,” Al-Nahrain University, College of Engineering Journal (NUCEJ), vol. 17, no.2, pp.151-156, 2014.
N. Koshev, E. S. Helou, E. X. Miqueles, Fast Backprojection Techniques for High Resolution Tomography, arXiv:1608.03589v1, 2016.
T. Ö. Onur, “An application of filtered back projection method for computed tomography images,” International Review of Applied Sciences and Engineering, vol. 12, issue 2, pp. 194-200, 2021. https://doi.org/10.1556/1848.2021.00231.
K. Cho, J. Chae, R.-Y. Kwon, S.-C. Bong, K.-S. Cho, “The application of the filtered backprojection algorithm to solar rotational tomography,” The Astrophysical Journal, vol.895, no. 1, pp. 1-12, 2020. https://doi.org/10.3847/1538-4357/ab88af.
W. R. Hendee, E. R. Ritenour, Medical Imaging Physics, John Wiley & Sons, Inc., 2002. https://doi.org/10.1002/0471221155.
D. M. Pelt, V. De Andrade, “Improved tomographic reconstruction of large-scale real-world data by filter optimization,” Adv Struct Chem Imag, vol. 2, article id: 17, 2016. https://doi.org/10.1186/s40679-016-0033-y.
G. T. Herman, Fundamentals of Computerized Tomography: Image Reconstruction from Projections, second edition, Springer, 2009.
D. F. G. de Azevedo, S. Helegda, P. C. Godoy, F. de Castro and M. C. de Castro, “Tomography simulation and reconstruction tools applied in the evaluation of parameters and techniques,” Proceedings of the 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, vol. 3, pp. 2260-2263, https://doi.org/10.1109/IEMBS.2001.1017224.
A. V. Oppenheim, and R. W. Schafer, Discrete-Time Signal Processing, Prentice-Hall, pp. 447-448, 1989.
S. A. Qureshi, S. M. Mirza and M. Arif, “Inverse radon transform-based image reconstruction using various frequency domain filters in parallel beam transmission tomography,” Proceedings of the 2005 IEEE Student Conference on Engineering Sciences and Technology, Karachi, Pakistan, 2005, pp. 1-8, https://doi.org/10.1109/SCONEST.2005.4382887.
X. Li, Y. He, Q. Hua, “Application of computed tomographic image reconstruction algorithms based on filtered back-projection in diagnosis of bone trauma diseases,” Journal of Medical Imaging and Health Informatics, vol. 10, no. 5, pp. 1219-1224, 2020. https://doi.org/10.1166/jmihi.2020.3036.
P. Prabhat, S. Arumugam, V. K. Madan, “Filtering in filtered backprojection computerized tomography,” Proceedings of the National Conference "NCNTE-2012" at Fr. C.R.I.T., Vashi, Navi Mumbai, Feb. 24-25, 2012.
D. M. Pelt, K. J. Batenburg, “Accurately approximating algebraic tomographic reconstruction by filtered backprojection,” Proceedings of the 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015, pp. 158–161.
R. Kumar, S. Hans, “Filtered back projection algorithm on computed tomography (CT) scan,” International Research Journal of Engineering and Technology, vol. 3, issue 6, pp. 2152-2156, 2016.
Z. Wang, J. Cai, W. Guo, M. Donnelley, D. Parsons, I. Lee, “Backprojection wiener deconvolution for computed tomographic reconstruction,” PLoS ONE, 13(12): e0207907, 2018. https://doi.org/10.1371/journal.pone.0207907.
L. Raczyński, W. Wiślicki, and et al, “Introduction of total variation regularization into filtered backprojection algorithm,” Acta Physica Polonica B, vol. 48, no. 10, pp. 1611-1618, 2017. https://doi.org/10.5506/APhysPolB.48.1611.
R. Maitra, “Efficient bandwidth estimation in 2D filtered backprojection reconstruction,” IEEE Transactions on Image Processing, vol. 28, no. 11, pp. 5610-5619, 2019, https://doi.org/10.1109/TIP.2019.2919428.
M. Andrew, B. Hornberger, Iterative Reconstruction for Optimized Tomographic Imaging, ZEISS Microscopy, Germany, 2018.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
