Artificial intelligence in digital holographic imaging : technical basis and biomedical applications
Record details
- ISBN: 9780470647509
- ISBN: 1119238951
- ISBN: 9781119238959
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Physical Description:
1 online resource (336 pages)
remote - Publisher: Hoboken, NJ : Wiley, [2023]
Content descriptions
Bibliography, etc. Note: | Includes bibliographical references and index. |
Formatted Contents Note: | Part I. Digital Holographic Microscopy (DHM) -- 1. Introduction -- References -- 2. Coherent optical imaging -- 2.1 Monochromatic fields and irradiance -- 2.2 Analytic expression for Fresnel diffraction -- 2.3 Transmittance function of lens -- 2.4 Geometrical imaging concepts -- 2.5 Coherent imaging theory -- References -- 3. Lateral and depth resolutions -- 3.1 Lateral resolution -- 3.2 Depth (or axial) resolution -- References -- 4. Phase unwrapping -- 4.1 Branch cuts -- 4.2 Quality-guided path-following algorithms -- References -- 5. Off-axis digital holographic microscopy -- 5.1 Off-axisdigital holographic microscopy designs -- 5.2 Digital hologram reconstruction -- References -- 6. Gabor digital holographic microscopy -- 6.1 Introduction -- 6.2 Methodology -- References -- -- Part II. Deep Learning in DHM Systems -- 7. Introduction -- References -- 8. No-search focus prediction in DHM with deep learning -- 8.1 Introduction -- 8.2 Materials and methods -- 8.3 Experimental results -- 8.4 Conclusions -- References -- 9. Automated phase unwrapping in DHM with deep learning -- 9.1 Introduction -- 9.2 Deep learning model -- 9.3 Unwrapping with deep learning model -- 9.4 Conclusions -- References -- 10. Noise-free phase imaging in Gabor DHM with deep learning -- 10.1 Introduction -- 10.2 A deep learning model for Gabor DHM -- 10.3 Experimental results -- 10.4 Discussion -- 10.5 Conclusions -- References -- -- Part III. Intelligent DHM for Biomedical Applications -- 11. Introduction -- References -- 12. Red blood cells phase image segmentation -- 12.1 Introduction -- 12.2 Marker-controlled watershed algorithm -- 12.3 Segmentation based on marker-controlled watershed algorithm -- 12.4 Experimental results -- 12.5 Performance evaluation -- 12.6 Conclusions -- References -- 13. Red blood cells phase image segmentation with deep learning -- 13.1 Introduction -- 13.2 Fully convolutional neural networks -- 13.3 Red blood cells phase image segmentation via deep learning -- 13.4 Experimental results -- 13.5 Conclusions -- References -- 14. Automated phenotypic classification of red blood cells -- 14.1 Introduction -- 14.2 Feature extraction -- 14.3 Pattern recognition neural network -- 14.4 Experimental results and discussion -- 14.5 Conclusions -- References -- 15. Automated analysis of red blood cell storage lesions -- 15.1 Introduction -- 15.2 Quantitative analysis of red blood cell 3D morphological changes -- 15.3 Experimental results and discussion -- 15.4 Conclusions -- References -- 16. Automated red blood cells classification with deep learning -- 16.1 Introduction -- 16.2 Proposed deep learning model -- 16.3 Experimental results -- 16.4 Conclusions -- References -- 17. High-throughput label-free cell counting with deep neural networks -- 17.1 Introduction -- 17.2 Materials and methods -- 17.3 Experimental results -- 17.4 Conclusions -- References -- 18. Automated tracking of temporal displacements of red blood cells -- 18.1 Introduction -- 18.2 Mean-shift tracking algorithm -- 18.3 Kalman filter -- 18.4 Procedure for single RBC tracking -- 18.5 Experimental results -- 18.6 Conclusions -- References -- 19. Automated quantitative analysis of red blood cells dynamics -- 19.1 Introduction -- 19.2 Red blood cell parameters -- 19.3 Quantitative analysis of red blood cell fluctuations -- 19.4 Conclusions -- References -- 20. Quantitative analysis of red blood cells during temperature elevation -- 20.1 Introduction -- 20.2 Red blood cell sample preparations -- 20.3 Experimental results -- 20.4 Conclusions -- References -- 21. Automated measurement of cardiomyocytes dynamics with DHM -- 21.1 Introduction -- 21.2 Cell culture and imaging -- 21.3 Automated analysis of cardiomyocytes dynamics -- 21.4 Conclusions -- References -- 22. Automated analysis of cardiomyocytes with deep learning -- 22.1 Introduction -- 22.2 Region of interest identification with dynamic beating activity analysis -- 22.3 Deep neural network for cardiomyocytes image segmentation -- 22.4 Experimental results -- 22.5 Conclusions -- References -- 23. Automatic quantification of drug-treated cardiomyocytes with DHM -- 23.1 Introduction -- 23.2 Materials and methods -- 23.3 Experimental results and discussion -- 23.4 Conclusions -- References -- 24. Analysis of cardiomyocytes with holographic image-based tracking -- 24.1 Introduction -- 24.2 Materials and methods -- 24.3 Experimental results and discussion -- 24.4 Conclusions -- References -- 25. Conclusion and future work. |
Source of Description Note: | Online resource; title from PDF title page (John Wiley, viewed November 18, 2022). |
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Subject: | Three-dimensional imaging Artificial intelligence Three-dimensional imaging in medicine Artificial intelligence Three-dimensional imaging Three-dimensional imaging in medicine |