• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0811006 (2022)
Zezheng Qin1, Mingjian Sun1、2、4、*, Yiming Ma1, Zhigang Lei2、3, and Yuanyuan Gao1
Author Affiliations
  • 1School of Information Science and Engineering, Harbin Institute of Technology, Weihai, Weihai , Shandong 264209, China
  • 2School of Astronautics, Harbin Institute of Technology, Harbin , Heilongjiang 150001, China
  • 3WEGO Holding Co., Ltd., Weihai , Shandong 213000, China
  • 4Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou , Jiangsu 215163, China
  • show less
    DOI: 10.3788/LOP202259.0811006 Cite this Article Set citation alerts
    Zezheng Qin, Mingjian Sun, Yiming Ma, Zhigang Lei, Yuanyuan Gao. Intelligent Denoising Algorithm for Signals Based on Three-Dimensional Photoacoustic Tomography System[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0811006 Copy Citation Text show less
    Overall flow chart of proposed algorithm
    Fig. 1. Overall flow chart of proposed algorithm
    New threshold processing function
    Fig. 2. New threshold processing function
    Comparison diagrams of different threshold functions. (a) Original signal; (b) noisy signal; (c) soft threshold denoising function; (d) proposed threshold denoising function; (e) hard threshold denoising function
    Fig. 3. Comparison diagrams of different threshold functions. (a) Original signal; (b) noisy signal; (c) soft threshold denoising function; (d) proposed threshold denoising function; (e) hard threshold denoising function
    Comparison of noisy photoacoustic signal and pure photoacoustic signal. (a) Pure photoacoustic signal;(b) noisy photoacoustic signal
    Fig. 4. Comparison of noisy photoacoustic signal and pure photoacoustic signal. (a) Pure photoacoustic signal;(b) noisy photoacoustic signal
    Decomposition diagram of CEEMDAN
    Fig. 5. Decomposition diagram of CEEMDAN
    Analysis of mutual information entropy of adjacent IMF components
    Fig. 6. Analysis of mutual information entropy of adjacent IMF components
    Correlation analysis
    Fig. 7. Correlation analysis
    IMF11 autocorrelation analysis
    Fig. 8. IMF11 autocorrelation analysis
    Dictionary atoms constituting pure photoacoustic signals
    Fig. 9. Dictionary atoms constituting pure photoacoustic signals
    Denoising effect of proposed algorithm
    Fig. 10. Denoising effect of proposed algorithm
    Comparison of denoising effects of different denoising algorithms
    Fig. 11. Comparison of denoising effects of different denoising algorithms
    Comparison of time-frequency domain analysis of photoacoustic signals for different denoising algorithms. (a) Denoising results of different denoising algorithms; (b) time-frequency distributions of photoacoustic signals based on different denoising algorithms
    Fig. 12. Comparison of time-frequency domain analysis of photoacoustic signals for different denoising algorithms. (a) Denoising results of different denoising algorithms; (b) time-frequency distributions of photoacoustic signals based on different denoising algorithms
    Comparison of imaging effects after denoising: (a) Photoacoustic image generated by pure photoacoustic signal; (b) photoacoustic image generated by noisy photoacoustic signal; (c) photoacoustic image after denoising.
    Fig. 13. Comparison of imaging effects after denoising: (a) Photoacoustic image generated by pure photoacoustic signal; (b) photoacoustic image generated by noisy photoacoustic signal; (c) photoacoustic image after denoising.
    Top view of full irradiated light path
    Fig. 14. Top view of full irradiated light path
    Schematic diagram of photoacoustic tomography system
    Fig. 15. Schematic diagram of photoacoustic tomography system
    Photoacoustic tomography system
    Fig. 16. Photoacoustic tomography system
    Phantom to be scanned and scanning position. (a) Tumor mimicry ; (b) scanning rendering; (c) enlarged effect diagram of dotted frame
    Fig. 17. Phantom to be scanned and scanning position. (a) Tumor mimicry ; (b) scanning rendering; (c) enlarged effect diagram of dotted frame
    Comparison of denoising effects. (a) Noisy photoacoustic signal reconstruction image; (b) photoacoustic signal reconstruction after denoising
    Fig. 18. Comparison of denoising effects. (a) Noisy photoacoustic signal reconstruction image; (b) photoacoustic signal reconstruction after denoising
    Photoacoustic imaging of each section and three-dimensional photoacoustic imaging effect. (a) Photoacoustic images of 42 sections; (b) three-dimensional photoacoustic imaging of tumor mimicry
    Fig. 19. Photoacoustic imaging of each section and three-dimensional photoacoustic imaging effect. (a) Photoacoustic images of 42 sections; (b) three-dimensional photoacoustic imaging of tumor mimicry
    Photoacoustic imageSNR /dBRMSE
    Before denoising8.684.59
    After denoising22.900.86
    Table 1. Comparison of photoacoustic image parameters before and after denoising
    Different cross-sectionVarianceContrast
    OriginalDenoisedOriginalDenoised
    Section 40.140.030.160.37
    Section 80.180.020.120.43
    Section 120.150.020.110.39
    Section 160.140.010.120.37
    Section 200.120.010.110.35
    Section 240.110.010.100.33
    Section 280.120.010.110.34
    Section 320.110.010.110.33
    Section 360.130.020.120.35
    Section 400.110.010.110.34
    Table 2. Comparison of photoacoustic image parameters before and after denoising
    Zezheng Qin, Mingjian Sun, Yiming Ma, Zhigang Lei, Yuanyuan Gao. Intelligent Denoising Algorithm for Signals Based on Three-Dimensional Photoacoustic Tomography System[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0811006
    Download Citation