Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
권호 표지
DOI QR코드

DOI QR Code

A Systematic Review of Trends for Image Quality Improvement in Light Microscopy

광학 현미경 영상 화질개선의 추세에 관한 체계적 고찰

  • Kyuseok Kim (Department of Biomedical Engineering, Eulji University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • 김규석 (을지대학교 의료공학과) ;
  • 이영진 (가천대학교 방사선학과)
  • Received : 2023.04.27
  • Accepted : 2023.05.22
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Image noise reduction algorithm performs important functions in light microscopy. This study aims to systematically review the research trend of types and performance evaluation methods of noise reduction algorithm in light microscopic images. A systematic literature search of three databases of publications from January 1985 to May 2020 was conducted; of the 139 publications reviewed, 16 were included in this study. For each research result, the subjects were categorized into four major frameworks-1. noise reduction method, 2. imaging technique, 3. imaging type, and 4. evaluation method-and analyzed. Since 2003, related studies have been conducted and published, and the number of papers has increased over the years and begun to decrease since 2016. The most commonly used method of noise reduction algorithm for light microscopy images was wavelet-transform-based technology, which was mostly applied in basic systems. In addition, research on the real experimental image was performed more actively than on the simulation condition, with the main case being to use the comparison parameter as an evaluation method. This systematic review is expected to be extremely useful in the future method of numerically analyzing the noise reduction efficiency of light microscopy images.

Keywords

References

  1. Davidson MW. Pioneers in optics: Zacharias Janssen and Johannes Kepler. Microscopy Today. 2009;17:44-6. https://doi.org/10.1017/S1551929509991052
  2. Davidson MW. Robert Hooke. Labmedicine. 2010;41:180-2. https://doi.org/10.1309/LMQ8H3HQHZQKECZZ
  3. Wimmer W. Carl Zeiss, Ernst Abbe, and advances in the light microscope. Microscopy Today. 2017;25:50-4. https://doi.org/10.1017/S155192951700058X
  4. Huang B, Babcock H, Zhuang X. Breaking the diffraction barrier: Super-resolution imaging of cells. Cell. 2010;143:1047-58. https://doi.org/10.1016/j.cell.2010.12.002
  5. Dedecker P, Hofkens J, Hotta JI. Left diffraction-unlimited optical microscopy. Materialstoday. 2008;11:12-21.
  6. Adhikari S, Moscatelli J, Smith EM, Banerjee C, Puchner EM. Single-molecule localization microscopy and tracking with red-shifted states of conventional BODIPY conjugates in living cells. Nature Communications. 2019;10. DOI: https://doi.org/10.1038/s41467-019-11384-6
  7. Moerner WE, Kador L. Optical detection and spectroscopy of single molecules in a solid. Physics Review Letters. 1989;62L2535-8.
  8. Moerner WE, Orrit M. Illuminating single molecules in condensed matter. Science. 1999;283:1670-6. https://doi.org/10.1126/science.283.5408.1670
  9. Meiniel W, Olivo-Marin JC, Angelini ED. Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method. IEEE Transactions on Image Processing. 2018;27:3842-56. https://doi.org/10.1109/TIP.2018.2819821
  10. Chambolle A. An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision. 2004;20:89-97.
  11. Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation. 2005;4:490-530. https://doi.org/10.1137/040616024
  12. Dautov CP, Ozerdem MS. Introduction to Wavelets and their applications in signal denoising. Bitlis Eren University Journal of Science and Technology. 2018;8:1-10. https://doi.org/10.17678/beuscitech.349020
  13. Srivastava M, Anderson CL, Freed JH. A new wavelet denoising method for selecting decomposition levels and noise thresholds. IEEE Access. 2016;4:3862-77. https://doi.org/10.1109/ACCESS.2016.2587581
  14. Wahab MF, O'Haver TC. Wavelet transforms in separation science for denoising and peak overlap detection. Journal of Separation Science. 2020;43:1998-2010. https://doi.org/10.1002/jssc.202000013
  15. Zhang M, Lu C, Liu C. Improved double-threshold denoising method based on the wavelet transform. OSA Continuum. 2019;2:2328-42. https://doi.org/10.1364/OSAC.2.002328
  16. Nowak RD. Wavelet-based rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing. 1999;8:1408-19. https://doi.org/10.1109/83.791966
  17. Zhang Y, Ding W, Pan Z, Qin J. Improved wavelet threshold for image de-noising. Frontiers in Neuroscience. 2019;13. DOI: https://doi.org/10.3389/fnins.2019.00039
  18. Donoho DL. De-noising by soft-thresholding. IEEE Transactions on Information Theory. 1995;41:613-27. https://doi.org/10.1109/18.382009
  19. AlAsadi AHH. Contourlet transform based method for medical image denoising. Journal of AL-Qadisiyah for Computer Science and Mathematics. 2015;7:1-7.
  20. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ. 2009;339:332-6. https://doi.org/10.1136/bmj.b2535
  21. Wang XH, Istepanian RSH, Song YH. Microarray image enhancement by denoising using stationary wavelet transform. IEEE Transactions on Nanobioscience. 2003;2:184-9. https://doi.org/10.1109/TNB.2003.816225
  22. Gopalappa C, Das TK, Enkemann S, Eschrich S. Removal of hybridization and scanning noise from microarrays. IEEE Transactions on Nanobioscience. 2009;8:210-8. https://doi.org/10.1109/TNB.2009.2029100
  23. Van't Hoff M, Reuter M, Dryden DTF, Oheim M. Screening by imaging: Scaling up single-DNA-molecule analysis with a novel parabolic VA-TIRF reflector and noise-reduction techniques. Physical Chemistry Chemical Physics. 2009;11:7713-20. https://doi.org/10.1039/b823155a
  24. Boulanger J, Kervrann C, Bouthemy P, Elbau P, Sibarita JB, Salamero J. Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Transactions on Medical Imaging. 2010;29:442-54. https://doi.org/10.1109/TMI.2009.2033991
  25. Howlader T, Chaubey YP. Noise reduction of cDNA microarray images using complex wavelets. IEEE Transactions on Image Processing. 2010;19:1953-67. https://doi.org/10.1109/TIP.2010.2045691
  26. Luisier F, Blu T, Unser M. Image denoising in mixed poisson-gaussian noise. IEEE Transactions on Image Processing. 2011;20:696-708. https://doi.org/10.1109/TIP.2010.2073477
  27. Chang CW, Mycek MA. Total variation versus wavelet-based methods for image denoising in fluorescence lifetime imaging microscopy. Journal of Biophotonics. 2013;249:449-57.
  28. Das DK, Chakraborty C, Mitra B, Maiti AK, Ray AK. Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia. Journal of Microscopy. 2013;249:136-49. https://doi.org/10.1111/jmi.12002
  29. Giryes R, Elad M. Sparsity based poisson denoising with dictionary learning. IEEE Transactions on Image Processing. 2014;23:5057-69. https://doi.org/10.1109/TIP.2014.2362057
  30. Lanza A, Morigi S, Sgallari F, Wen YW. Image restoration with Poisson-Gaussian mixed noise. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2014;2:12-24. https://doi.org/10.1080/21681163.2013.811039
  31. Yoon JW. Statistical denoising scheme for single molecule fluorescence microscopic images. Biomedical Signal Processing and Control. 2014;10:11-20. https://doi.org/10.1016/j.bspc.2013.12.005
  32. Bernas T, Starosolski R, Wojcicki R. Application of detector precision characteristics for the denoising of biological micrographs in the wavelet domain. Biomedical Signal Processing and Control. 2015;19:1-28. https://doi.org/10.1016/j.bspc.2015.02.010
  33. Yang S, Lee BU. Poisson-Gaussian noise reduction using the hidden markov model in contourlet domain for fluorescence microscopy images. PLoS One. 2015;10. DOI: https://doi.org/10.1371/journal.pone.0136964
  34. Bai C, Liu C, Jia H, Peng T, Min J, Lei M, Yu X, Yao B. Compressed blind deconvolution and denoising for complementary beam subtraction light-sheet fluorescence microscopy. IEEE Transactions on Biomedical Engineering. 2019;66:2979-89. https://doi.org/10.1109/TBME.2019.2899583
  35. Xiao C, Smith ZJ, Chu K. Simultaneous recovery of both bright and dim structures from noisy fluorescence microscopy images using a modified TV constraint. Journal of Microscopy. 2019;275:24-35. https://doi.org/10.1111/jmi.12799
  36. Kim JY, Lee Y. Preliminary study of improved median filter using adaptively mask size in light microscopic image. Microscopy. 2020;69:31-6. https://doi.org/10.1093/jmicro/dfz111