Read more
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...
