Artificial intelligence in digital holographic imaging technical basis and biomedical applications
Artificial Intelligence in Digital Holographic Imaging Technical Basis and Biomedical Applications An eye-opening discussion of 3D optical sensing, imaging, analysis, and pattern recognition Artificial intelligence (AI) has made great progress in recent years. Digital holographic imaging has recentl...
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| Format: | eBook |
| Language: | English |
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Hoboken, NJ
Wiley
2023
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| Series: | Wiley series in biomedical engineering and multi-disciplinary integrated systems
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- Includes bibliographical references and index
- 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
- 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
- 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
- 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