Artificial Intelligence in Digital Holographic Imaging

Artificial Intelligence in Digital Holographic Imaging

by Inkyu Moon
Released December 2022
Publisher(s): Wiley
ISBN: 9780470647509

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Book description

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 recently emerged as a powerful new technique well suited to explore cell structure and dynamics with a nanometric axial sensitivity and the ability to identify new cellular biomarkers. By combining digital holography with AI technology, including recent deep learning approaches, this system can achieve a record-high accuracy in non-invasive, label-free cellular phenotypic screening. It opens up a new path to data-driven diagnosis.

Artificial Intelligence in Digital Holographic Imaging introduces key concepts and algorithms of AI to show how to build intelligent holographic imaging systems drawing on techniques from artificial neural networks, convolutional neural networks, and generative adversarial network. Readers will be able to gain an understanding of the basics for implementing AI in holographic imaging system designs and connecting practical biomedical questions that arise from the use of digital holography with various AI algorithms in intelligence models.

What’s Inside

  • Introductory background on digital holography
  • Key concepts of digital holographic imaging
  • Deep-learning techniques for holographic imaging
  • AI techniques in holographic image analysis
  • Holographic image-classification models
  • Automated phenotypic analysis of live cells

For readers with various backgrounds, this book provides a detailed discussion of the use of intelligent holographic imaging system in biomedical fields with great potential for biomedical application.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Preface
  5. Part I: Digital Holographic Imaging
    1. 1 Introduction
      1. References
    2. 2 Coherent Optical Imaging
      1. 2.1 Monochromatic Fields and Irradiance
      2. 2.2 Analytic Expression for Fresnel Diffraction
      3. 2.3 Lens Transmittance Function
      4. 2.4 Geometrical Imaging Concepts
      5. 2.5 Coherent Imaging Theory
      6. References
    3. 3 Lateral and Depth Resolutions
      1. 3.1 Lateral Resolution
      2. 3.2 Depth (or Axial) Resolution
      3. References
    4. 4 Phase Unwrapping
      1. 4.1 Branch Cuts
      2. 4.2 Quality‐guided, Path‐following Algorithms
      3. References
    5. 5 Off‐axis Digital Holographic Microscopy
      1. 5.1 Off‐axis Digital Holographic Microscopy Designs
      2. 5.2 Digital Hologram Reconstruction
      3. References
    6. 6 Gabor Digital Holographic Microscopy
      1. 6.1 Introduction
      2. 6.2 Methodology
      3. References
  6. Part II: Deep Learning in Digital Holographic Microscopy (DHM)
    1. 7 Introduction
      1. References
    2. 8 No‐search Focus Prediction in DHM with Deep Learning
      1. 8.1 Introduction
      2. 8.2 Materials and Methods
      3. 8.3 Experimental Results
      4. 8.4 Conclusions
      5. References
    3. 9 Automated Phase Unwrapping in DHM with Deep Learning
      1. 9.1 Introduction
      2. 9.2 Deep‐learning Model
      3. 9.3 Unwrapping with Deep‐learning Model
      4. 9.4 Conclusions
      5. References
    4. 10 Noise‐free Phase Imaging in Gabor DHM with Deep Learning
      1. 10.1 Introduction
      2. 10.2 A Deep‐learning Model for Gabor DHM
      3. 10.3 Experimental Results
      4. 10.4 Discussion
      5. 10.5 Conclusions
      6. References
  7. Part III: Intelligent Digital Holographic Microscopy (DHM) for Biomedical Applications
    1. 11 Introduction
      1. References
    2. 12 Red Blood Cell Phase‐image Segmentation
      1. 12.1 Introduction
      2. 12.2 Marker‐controlled Watershed Algorithm
      3. 12.3 Segmentation Based on Marker‐controlled Watershed Algorithm
      4. 12.4 Experimental Results
      5. 12.5 Performance Evaluation
      6. 12.6 Conclusions
      7. References
    3. 13 Red Blood Cell Phase‐image Segmentation with Deep Learning
      1. 13.1 Introduction
      2. 13.2 Fully Convolutional Neural Networks
      3. 13.3 RBC Phase‐image Segmentation via Deep Learning
      4. 13.4 Experimental Results
      5. 13.5 Conclusions
      6. References
    4. 14 Automated Phenotypic Classification of Red Blood Cells
      1. 14.1 Introduction
      2. 14.2 Feature Extraction
      3. 14.3 Pattern Recognition Neural Network
      4. 14.4 Experimental Results and Discussion
      5. 14.5 Conclusions
      6. References
    5. 15 Automated Analysis of Red Blood Cell Storage Lesions
      1. 15.1 Introduction
      2. 15.2 Quantitative Analysis of RBC 3D Morphological Changes
      3. 15.3 Experimental Results and Discussion
      4. 15.4 Conclusions
      5. References
    6. 16 Automated Red Blood Cell Classification with Deep Learning
      1. 16.1 Introduction
      2. 16.2 Proposed Deep‐learning Model
      3. 16.3 Experimental Results
      4. 16.4 Conclusions
      5. References
    7. 17 High‐throughput Label‐free Cell Counting with Deep Neural Networks
      1. 17.1 Introduction
      2. 17.2 Materials and Methods
      3. 17.3 Experimental Results
      4. 17.4 Conclusions
      5. References
    8. 18 Automated Tracking of Temporal Displacements of Red Blood Cells
      1. 18.1 Introduction
      2. 18.2 Mean‐shift Tracking Algorithm
      3. 18.3 Kalman Filter
      4. 18.4 Procedure for Single RBC Tracking
      5. 18.5 Experimental Results
      6. 18.6 Conclusions
      7. References
    9. 19 Automated Quantitative Analysis of Red Blood Cell Dynamics
      1. 19.1 Introduction
      2. 19.2 RBC Parameters
      3. 19.3 Quantitative Analysis of RBC Fluctuations
      4. 19.4 Conclusions
      5. References
    10. 20 Quantitative Analysis of Red Blood Cells during Temperature Elevation
      1. 20.1 Introduction
      2. 20.2 RBC Sample Preparations
      3. 20.3 Experimental Results
      4. 20.4 Conclusions
      5. References
    11. 21 Automated Measurement of Cardiomyocyte Dynamics with DHM
      1. 21.1 Introduction
      2. 21.2 Cell Culture and Imaging
      3. 21.3 Automated Analysis of Cardiomyocyte Dynamics
      4. 21.4 Conclusions
      5. References
    12. 22 Automated Analysis of Cardiomyocytes with Deep Learning
      1. 22.1 Introduction
      2. 22.2 Region‐of‐interest Identification with Dynamic Beating Activity Analysis
      3. 22.3 Deep Neural Network for Cardiomyocyte Image Segmentation
      4. 22.4 Experimental Results
      5. 22.5 Conclusions
      6. References
    13. 23 Automatic Quantification of Drug‐treated Cardiomyocytes with DHM
      1. 23.1 Introduction
      2. 23.2 Materials and Methods
      3. 23.3 Experimental Results and Discussion
      4. 23.4 Conclusions
      5. References
    14. 24 Analysis of Cardiomyocytes with Holographic Image‐based Tracking
      1. 24.1 Introduction
      2. 24.2 Materials and Methods
      3. 24.3 Experimental Results and Discussion
      4. 24.4 Conclusions
      5. References
    15. 25 Conclusion and Future Work
  8. Index
  9. End User License Agreement

Product information

  • Title: Artificial Intelligence in Digital Holographic Imaging
  • Author(s): Inkyu Moon
  • Release date: December 2022
  • Publisher(s): Wiley
  • ISBN: 9780470647509