Implementation of a Selective Median Filter in Computed Tomography for Image Quality Enhancement

Authors

  • Amril Mukmin  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Choirul Anam  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Catur E. Widodo  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Ariij Naufal  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Fakhri R. Arisyi  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia

DOI:

https://doi.org/10.32628/IJSRST229485

Keywords:

Selective Median Filter, Spatial Resolution, SNR, CNR, Noise Reduction

Abstract

Noise in computed tomography (CT) images is unavoidable due to various factors such as errors from patient sources, hardware errors, and image acquisition. In order to reduce the noise, a selective median filter (SMeF) has been developed. SMeF determine the median value selectively using a threshold. For quantitative evaluation, effectiveness of the filter algorithm was assessed using metrics of modulation transfer function (MTF) and contrast-to-noise ratio (CNR) obtained from phantom and head CT images. The results of the SMeF assessment were compared with the original image and images filtered with traditional median filter (MeF), selective mean filter (SMF) and bilateral filter (BF). It was found that the noise levels of images filtered with SMeF are lower compared to those filtered with MeF, BF, and SMF. SMeF has a relatively good value in terms of CNR. The spatial resolution of images filtered with SMeF is comparable compared spatial resolutions of the original image, MeF, SMF and BF. The SMeF is an effective algorithm in reducing noise of CT phantom images and clinical images.

References

  1. Kaur P, Singh G, Kaur P. A Review of Denoising Medical Images Using Machine Learning Approaches. Curr Med Imaging Rev. 2018;14(5):675-685.
  2. Amir NSS, Kang LZ, Mukari SA, Sahathevan R, Chellappan K. CT brain image advancement for ICH diagnosis. Healthc Technol Lett. 2019;7(1):1-6.
  3. Yao H, Wang S, Zhang X, et al. Detecting Image Splicing Based on Noise Level Inconsistency. Multimed Tools Appl. 2017;76:12457-12479.
  4. Diwakar M & Kumar M. A review on CT image noise and its denoising. Biomedical Signal Processing and Control. 2018;42:73-88.
  5. Anam C, Adi K, Sutanto H, et al. Noise Reduction in CT Images Using a Selective Mean Filter. J Biomed Phys Eng. 2020;10(5):623-634
  6. Prell D, Kyriakou Y, Kalender WA. Comparison of ring artifact correction methods for flat-detector CT. Phys Med Biol. 2009;54(12):3881-3895.
  7. Manduca A, Yu L, Trzasko JD, et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys. 2009;36(11):4911-4919.
  8. Fan H & Zhu H. Preservation of image edge feature based on snowfall model smoothing filter. J Image Video Proc. 2018; 67(2018).
  9. Nishimaru E, Ichikawa K, Okita I, et al. Development of a noise reduction filter algorithm for pediatric body images in multidetector CT. J Digit Imaging. 2010;23(6):806-818.
  10. Sadri AR, Zekri M, Sadri S, Gheissari N. Impulse noise cancellation of medical images using wavelet networks and median filters. J Med Signals Sens. 2012;2(1):25-37.
  11. Saragih JH, Anam C, Budi WS, Zahro UM, Dougherty G. Effectiveness of a selected mean filter algorithm to reduce noise in fluoroscopy images. International Journal of Scientific & Technology Research. 2020;1(1):231-234.
  12. Saragih JH, Anam C, Budi WS, Simarmata TI, Dougherty G. Implementation of a selective mean filter algorithm to reduce noise in computed radiography images. AIP Conf. Proc. 2021;2346: 040006.
  13. Geyer LL, Schoepf UJ, Meinel FG, et al. State of the Art: Iterative CT Reconstruction Techniques. Radiology. 2015;276(2):339-357.
  14. Patino M, Fuentes JM, Singh S, Hahn PF, Sahani DV. Iterative Reconstruction Techniques in Abdominopelvic CT: Technical Concepts and Clinical Implementation. AJR Am J Roentgenol. 2015;205(1):W19-W31.
  15. Ichikawa K, Kawashima H, Shimada M, Adachi T, Takata T. A three-dimensional cross-directional bilateral filter for edge-preserving noise reduction of low-dose computed tomography images. Comput Biol Med. 2019;111:103353.
  16. Pan S, An X, He H. Optimal O(1) bilateral filter with arbitrary spatial and range kernels using sparse approximation. Math. Probl. Eng. 2014;2014:289517.
  17. He Y, Zheng Y, Zhao Y, Ren Y, Lian J, Gee J. Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function. Comput Math Methods Med. 2017;2017:1769834.
  18. van Ommen F, Bennink E, Vlassenbroek A, et al. Image quality of conventional images of dual-layer SPECTRAL CT: A phantom study. Med Phys. 2018;45(7):3031-3042.
  19. Khoramian D, Sistani S, Firouzjah RA. Assessment and comparison of radiation dose and image quality in multi-detector CT scanners in non-contrast head and neck examinations. Pol J Radiol. 2019;84:e61-e67.
  20. Wu D, Wang G, Bian B, Liu Z, Li D. Benefits of Low-Dose CT Scan of Head for Patients With Intracranial Hemorrhage. Dose Response. 2020;19(1):1559325820909778.
  21. Greffier J, Frandon J, Pereira F, et al. Optimization of radiation dose for CT detection of lytic and sclerotic bone lesions: a phantom study. Eur Radiol. 2020;30(2):1075-1078.
  22. Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020;47(9):3961-3971.
  23. Sugisawa K, Ichikawa K, Urikura A, et al. Spatial resolution compensation by adjusting the reconstruction kernels for iterative reconstruction images of computed tomography. Phys Med. 2020;74:47-55.
  24. Barca P, Paolicchi F, Aringhieri G, et al. A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study. PLoS One. 2021;16(1):e0245374.
  25. Zahro UM, Anam C, Budi WS, Triadyaksa P, Saragih JH. Investigation of noise level and spatial resolution of CT images filtered with a selective mean filter and its comparison to an adaptive statistical iterative reconstruction. Iran. J. Med. Phys. 2020;18(5):376-383.
  26. Greffier J, Fernandez A, Macri F, Freitag C, Metge L, Beregi JP. Which dose for what image? Iterative reconstruction for CT scan. Diagn Interv Imaging. 2013;94(11):1117-1121.
  27. Baskan O, Erol C, Ozbek H, Paksoy Y. Effect of radiation dose reduction on image quality in adult head CT with noise-suppressing reconstruction system with a 256 slice MDCT. J Appl Clin Med Phys. 2015;16(3):5360.
  28. Greffier J, Frandon J, Larbi A, Beregi JP, Pereira F. CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol. 2020;30(1):487-500.
  29. Yuan Q, Peng Z, Chen Z, Guo Y, Yang B, Zeng X, Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Noise reduction Algorithm. J. Healthc. Eng. 2021;2021:6095676.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2022-08-30

Issue

Volume 9, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Amril Mukmin, Choirul Anam, Catur E. Widodo, Ariij Naufal, Fakhri R. Arisyi "Implementation of a Selective Median Filter in Computed Tomography for Image Quality Enhancement " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 4, pp.545-551, July-August-2022. Available at doi : https://doi.org/10.32628/IJSRST229485