• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0237007 (2025)
Hao Yang1、2、*, Shengbing Shi3, Liangliang Yao4, Dian Gui1、2, Lu Shi1、2, Jinyu Zhao1, and Haoran Meng1
Author Affiliations
  • 1Key Laboratory of Advanced Manufacturing of Optical Systems, Changchun Institute of Optics, Precision Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Jilin , China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Chinese People's Liberation Army Unit 63850, Baicheng 137000, Jilin , China
  • 4Army Equipment Department Military Representative Office in Chongqing Region, Kunming Branch, Kunming 650000, Yunnan , China
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    DOI: 10.3788/LOP241162 Cite this Article Set citation alerts
    Hao Yang, Shengbing Shi, Liangliang Yao, Dian Gui, Lu Shi, Jinyu Zhao, Haoran Meng. Microorganism Contour Segmentation Method Using Imaging Models and Fourier Descriptors[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237007 Copy Citation Text show less
    Underwater in situ holographic imaging system. (a) System optical path structure; (b) schematic of optical path; (c) physical drawing
    Fig. 1. Underwater in situ holographic imaging system. (a) System optical path structure; (b) schematic of optical path; (c) physical drawing
    Partial holograms captured by the system
    Fig. 2. Partial holograms captured by the system
    Reconstruction results of hologram. (a) Pre-reconstruction resolution plate image; (b) post-reconstruction resolution plate image; (c) pre-reconstruction biological specimen image; (d) post-reconstruction biological specimen image; (e) pre-reconstruction underwater microbial 1 image; (f) post-reconstruction underwater microbial 1 image; (g) pre-reconstruction underwater microbial 2 image; (h) post-reconstruction underwater microbial 2 image
    Fig. 3. Reconstruction results of hologram. (a) Pre-reconstruction resolution plate image; (b) post-reconstruction resolution plate image; (c) pre-reconstruction biological specimen image; (d) post-reconstruction biological specimen image; (e) pre-reconstruction underwater microbial 1 image; (f) post-reconstruction underwater microbial 1 image; (g) pre-reconstruction underwater microbial 2 image; (h) post-reconstruction underwater microbial 2 image
    Comparison of interference fringe filtering spectra. (a) Spatial domain image before filtering; (b) frequency domain image before filtering; (c) spatial domain image after filtering by traditional method; (d) frequency domain image after filtering by traditional method; (e) spatial domain image after filtering by improved method; (f) frequency domain image after filtering by improved method
    Fig. 4. Comparison of interference fringe filtering spectra. (a) Spatial domain image before filtering; (b) frequency domain image before filtering; (c) spatial domain image after filtering by traditional method; (d) frequency domain image after filtering by traditional method; (e) spatial domain image after filtering by improved method; (f) frequency domain image after filtering by improved method
    Interference stripe filtering effect. (a) Resolution plate image before filtering; (b) resolution plate image after filtering; (c) biological specimen image before filtering; (d) post-filtered biological specimen image; (e) pre-filtered biological specimen image; (f) post-filtered biological specimen image
    Fig. 5. Interference stripe filtering effect. (a) Resolution plate image before filtering; (b) resolution plate image after filtering; (c) biological specimen image before filtering; (d) post-filtered biological specimen image; (e) pre-filtered biological specimen image; (f) post-filtered biological specimen image
    Underwater microbial contour segmentation network architecture
    Fig. 6. Underwater microbial contour segmentation network architecture
    Structure diagram of background scattering estimation module
    Fig. 7. Structure diagram of background scattering estimation module
    Structure diagram of direct transmission estimation module
    Fig. 8. Structure diagram of direct transmission estimation module
    Network structure of contour segmentation task
    Fig. 9. Network structure of contour segmentation task
    Local refinement algorithm
    Fig. 10. Local refinement algorithm
    Image fusion effect. (a) (b) (c) Images without water; (d) (e) (f) images with water; (g) (h) (i) annotation
    Fig. 11. Image fusion effect. (a) (b) (c) Images without water; (d) (e) (f) images with water; (g) (h) (i) annotation
    Comparison of contour representation capabilities
    Fig. 12. Comparison of contour representation capabilities
    Experimental errors. (a) Regularly shaped microorganisms; (b) irregularly shaped microorganisms
    Fig. 13. Experimental errors. (a) Regularly shaped microorganisms; (b) irregularly shaped microorganisms
    Comparison of spatial continuity
    Fig. 14. Comparison of spatial continuity
    Errors of spatial continuity
    Fig. 15. Errors of spatial continuity
    Segmentation results of different algorithms
    Fig. 16. Segmentation results of different algorithms
    ItemTrue value
    PN
    Predictive valueP'TPFP
    N'FNTN
    Table 1. Confusion matrix
    IndicatorU-NetMask-R-CNNStarDistProposed
    F1 score0.81780.85270.85390.8894
    IoU0.76580.74230.77830.7887
    PA0.79030.82510.85990.8608
    Table 2. Comparison of experimental results
    Hao Yang, Shengbing Shi, Liangliang Yao, Dian Gui, Lu Shi, Jinyu Zhao, Haoran Meng. Microorganism Contour Segmentation Method Using Imaging Models and Fourier Descriptors[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237007
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