대한방사선기술학회지:방사선기술과학 (Journal of radiological science and technology)

  • 제45권2호
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  • Pages.145-150
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  • 2022
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  • 2288-3509(pISSN)
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  • 2384-1168(eISSN)
대한방사선과학회 (Korean Society of Radiological Science)
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흉부 컴퓨터단층촬영 영상에서 블라인드 디컨볼루션 알고리즘 최적화 방법에 대한 연구

Analysis on Optimal Approach of Blind Deconvolution Algorithm in Chest CT Imaging

  • Lee, Young-Jun (Department of Radiology, The Seoul National University Hospital of Korea) ;
  • Min, Jung-Whan (Department of Radiological Technology The Shingu University of Korea)
  • 투고 : 2022.03.08
  • 심사 : 2022.04.04
  • 발행 : 2022.04.30
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초록

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

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참고문헌

  1. Fokkens WJ, Lund VJ, Mullol J, Bachert C, Alobid I, Baroody F, et al. EPOS 2012: European position paper on rhinosinusitis and nasal polyps 2012. A Summary for Otorhinolaryngologists. Rhinology. 2012;50(1):1-12. https://doi.org/10.4193/Rhino50E2
  2. Goo HW. CT radiation dose optimization and estimation: An update for radiologists. Korean Journal of Radiology. 2012;13(1):1-11. https://doi.org/10.3348/kjr.2012.13.1.1
  3. Gunn ML, Kohr JR. State of the art: Technologies for computed tomography dose reduction. Emergency Radiology. 2010;17(3):209-18. https://doi.org/10.1007/s10140-009-0850-6
  4. Kalra MK, Maher MM, Toth TL, Hamberg LM, Blake MA, Shepard JA, et al. Strategies for CT radiation dose optimization. Radiology. 2004;230(3):619-28. https://doi.org/10.1148/radiol.2303021726
  5. McCollough CH, Bruesewitz MR, Kofler Jr. JM. CT dose reduction and dose management tools: Overview of available options. Radiographics. 2006;26(2):503-12. https://doi.org/10.1148/rg.262055138
  6. Beister M, Kolditz D, Kalender WA. Iterative reconstruction methods in X-ray CT. Physica Medica. 2012;28(2):94-108. https://doi.org/10.1016/j.ejmp.2012.01.003
  7. Fleischmann D, Boas FE. Computed tomography-old ideas and new technology. European Radiology. 2011;21(3):510-7. https://doi.org/10.1007/s00330-011-2056-z
  8. Kalra MK, WoisetschlWger M, DahlstrWm N, Singh S, Lindblom M, Choy G, et al. Radiation dose reduction with sinogram affirmed iterative reconstruction technique for abdominal computed tomography. Journal of Computer Assisted Tomography. 2012;36(3):339-46. https://doi.org/10.1097/RCT.0b013e31825586c0
  9. Leipsic J, Heilbron BG, Hague C. Iterative reconstruction for coronary CT angiography: Finding its way. The International Journal of Cardiovascular Imaging. 2012;28(3):613-20. https://doi.org/10.1007/s10554-011-9832-3
  10. NoWl PB, Fingerle AA, Renger B, MWnzel D, Rummeny EJ, Dobritz M. Initial performance characterization of a clinical noise-suppressing reconstruction algorithm for mdct. American Journal of Roentgenology. 2011;197(6):1404-9. https://doi.org/10.2214/AJR.11.6907
  11. Schuler CJ, Hirsch M, Harmeling S, SchWlkopf B. Learning to deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015;38(7):1439-51. https://doi.org/10.1109/TPAMI.2015.2481418
  12. Chakrabarti A. ed. A neural approach to blind motion deblurring. European Conference on Computer Vision. Springer; 2016.
  13. Sun J, Cao W, Xu Z, Ponce J. eds. Learning a convolutional neural network for non-uniform motion blur removal. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  14. Kundur D, Hatzinakos D. Blind image deconvolution. IEEE Signal Processing Magazine. 1996a;13(3):43-64. https://doi.org/10.1109/79.489268
  15. Kundur D, Hatzinakos D. Blind image deconvolution revisited. IEEE Signal Processing Magazine. 1996b;13(6):61-3. https://doi.org/10.1109/79.543976
  16. Chae KJ, Goo JM, Ahn SY, Yoo JY, Yoon SH. Application of Deconvolution Algorithm of Point Spread Function in Improving Image Quality: An Observer Preference Study on Chest Radiography. Korean J Radiol. 2018;19(1):147-152. https://doi.org/10.3348/kjr.2018.19.1.147
  17. Kaushik P, Chawla M, Kumar G. Detection of Noise in an Image using Blind Deconvolution Method. International Journal of Scientific Research and Management. 2020;3:3411-5.
  18. Sharma K, Kundra S. Restoration of Medical Images using Blind Image Deconvolution based on Ant Colony. International Journal of Computer Applications. 2013;84:24-7. https://doi.org/10.5120/14661-2957
  19. Lee YJ, Min JW. Comparison of Based on Histogram Equalization Techniques by Using Normalization in Thoracic Computed Tomography. Journal of Radiological Science and Technology. 2021;44(5):473-80. https://doi.org/10.17946/JRST.2021.44.5.473
  20. Min JW, Jeong HW. Evaluation of Resolution Characteristics by Using Chart Device Angle. Journal of Radiological Science and Technology. 2021;44(4):375-80. https://doi.org/10.17946/JRST.2021.44.4.375