Artificial Intelligence in Digital Holographic Imaging

Cover
Title Page
Copyright Page
Contents
Preface
Part I Digital Holographic Imaging
	Chapter 1 Introduction
		References
	Chapter 2 Coherent Optical Imaging
		2.1  Monochromatic Fields and Irradiance
		2.2  Analytic Expression for Fresnel Diffraction
		2.3  Lens Transmittance Function
		2.4  Geometrical Imaging Concepts
		2.5  Coherent Imaging Theory
		References
	Chapter 3 Lateral and Depth Resolutions
		3.1  Lateral Resolution
		3.2  Depth (or Axial) Resolution
		References
	Chapter 4 Phase Unwrapping
		4.1  Branch Cuts
		4.2  Quality-guided, Path-following Algorithms
		References
	Chapter 5 Off-axis Digita Holographi Microscopy
		5.1  Off-axis Digita Holographi Microscop Designs
		5.2  Digita Hologra Reconstruction
		References
	Chapter 6 Gabo Digita Holographi Microscopy
		6.1  Introduction
		6.2  Methodology
		References
Par II Dee Learning in Digital Holographic Microscopy (DHM)
	Chapter 7 Introduction
		References
	Chapter 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
	Chapter 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
	Chapter 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 Digital Holographic Microscopy (DHM) for Biomedical Applications
	Chapter 11 Introduction
		References
	Chapter 12 Red Blood Cell 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
	Chapter 13 Red Blood Cell Phase-image Segmentation with Deep Learning
		13.1  Introduction
		13.2  Fully Convolutional Neural Networks
		13.3  RBC Phase-image Segmentation via Deep Learning
		13.4  Experimental Results
		13.5  Conclusions
		References
	Chapter 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
	Chapter 15 Automated Analysis of Red Blood Cell Storage Lesions
		15.1  Introduction
		15.2  Quantitative Analysis of RBC 3D Morphological Changes
		15.3  Experimental Results and Discussion
		15.4  Conclusions
		References
	Chapter 16 Automated Red Blood Cell Classification with Deep Learning
		16.1  Introduction
		16.2  Proposed Deep-learning Model
		16.3  Experimental Results
		16.4  Conclusions
		References
	Chapter 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
	Chapter 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
	Chapter 19 Automated Quantitative Analysis of Red Blood Cell Dynamics
		19.1  Introduction
		19.2  RBC Parameters
		19.3  Quantitative Analysis of RBC Fluctuations
		19.4  Conclusions
		References
	Chapter 20 Quantitative Analysis of Red Blood Cells during Temperature Elevation
		20.1  Introduction
		20.2  RBC Sample Preparations
		20.3  Experimental Results
		20.4  Conclusions
		References
	Chapter 21 Automated Measurement of Cardiomyocyte Dynamics with DHM
		21.1  Introduction
		21.2  Cell Culture and Imaging
		21.3  Automated Analysis of Cardiomyocyte Dynamics
		21.4  Conclusions
		References
	Chapter 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 Cardiomyocyte Image Segmentation
		22.4  Experimental Results
		22.5  Conclusions
		References
	Chapter 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
	Chapter 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
	Chapter 25 Conclusion and Future Work
Index
EULA