Artificial Intelligence in Digital Holographic Imaging: Technical Basis and Biomedical Applications
by Inkyu Moon
- Length: 352 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-12-20
- ISBN-10: 0470647507
- ISBN-13: 9780470647509
- Sales Rank: #0 (See Top 100 Books)
This book presents a ground-breaking intelligent system for fast and non-invasive microbial identification using 3D optical imaging methods and high throughput algorithms for automatic analysis of 3D and 4D microscopic image data, as well as analysis of microscopic imaging towards a basic understanding of biological specimens. This process has applications in everything from the detection of biological weapons to the diagnosis of diseases. Designed for self-study as well as the classroom, this book is a vital tool for scientists and engineers.
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
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