Artificial Intelligence in Digital Holographic Imaging - Technical Basis and Biomedical Applications

Informationen zum Autor Inkyu Moon, PhD , is Professor in the Department of Robotics and Mechatronics Engineering at Daegu Gyeongbuk Institute of Science & Technology (DGIST), South Korea. Klappentext Artificial Intelligence in Digital Holographic Imaging Technical Basis and Biomedical ApplicationsAn eye-opening discussion of 3D optical sensing, imaging, analysis, and pattern recognitionArtificial 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 cellsFor 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. Zusammenfassung 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. Inhaltsverzeichnis 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 Co...

List 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
 
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
 

Product details

• Authors: Inkyu Moon, Inkyu (Daegu Gyeongbuk Institute of Science Moon
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 13.12.2023
• EAN: 9780470647509
• ISBN: 978-0-470-64750-9
• No. of pages: 336
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Künstliche Intelligenz, KI, AI, Signalverarbeitung, Signal Processing, Biomedical engineering, Imaging systems & technology, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Bildgebende Verfahren i. d. Biomedizin, Biomedical Imaging, Biomedizintechnik, Bildgebende Systeme u. Verfahren, Holographie