Autorentext

Ayush Dogra, PhD, is an Assistant Director at Chitkara University, Punjab, India. His research areas include image fusion, image enhancement, image registration, and image denoising. Shalli Rani, PhD, is a Professor and Director at Chitkara University, Punjab, India. She is a Senior Member of the IEEE and her research interests include Internet of Things, WSN, cloud computing, network security, and machine learning. Ankita Sharma, PhD, is an Assistant Professor at Chitkara University, Punjab, India. She has authored numerous national and international publications in peer-reviewed journals.

Klappentext

Explore emerging applications for AI, machine learning, and deep learning in biomedical imaging technologies In Biomedical Imaging Technology, a team of distinguished researchers deliver an expert discussion on the application of imaging and signal processing techniques to healthcare technologies like X-ray, MRI, CT, ultrasound, and others. Beginning with an introduction to biomedical imaging, the book goes on to explain more advanced imaging technologies, such as molecular and optical imaging. This book provides a blend of theory and practical applications, exploring the role of AI and AI algorithms in enhancing diagnostic accuracy. It discusses machine and deep learning approaches for improving computer-aided diagnosis systems and the integration of signal processing within various imaging modalities. Readers will also find:

• A thorough introduction to contemporary approaches to optical imaging, including fluorescence imaging, photoacoustic imaging, and Optical Coherence Tomography (OCT)
• Comprehensive explorations of image-guided interventions, theranostics in cancer treatment, and advancements in surgical navigation
• Practical discussions of emerging trends in the field and up-and-coming innovations

• Case studies and practical examples from real-world locations Perfect for researchers in biomedical engineering, imaging, and signal processing, Biomedical Imaging Technology will also benefit undergraduate and graduate students studying electrical engineering subjects, such as biomedical imaging and signal processing.

  Inhalt

  List of Contributors xix

  About the Editors xxii

  Preface xxv

  Acknowledgments xxvi

  1 Historical Evolution and Technological Advancements in Biomedical Imaging 1
  Shubham Gupta and Suhaib Ahmed

  1.1 Introduction 1

  1.2 Early Milestones in Biomedical Imaging 5

  1.2.1 Pre-Imaging Era: Anatomy and Physical Diagnosis 5

  1.2.2 Discovery of X-Rays and Birth of Radiography 7

  1.2.3 Development of Radioisotope Imaging (Nuclear Medicine) 7

  1.3 Signal Processing Strategies in Biomedical Imaging 8

  1.3.1 Data Acquisition and Preprocessing 8

  1.3.2 Image Reconstruction Algorithms 9

  1.3.3 Feature Extraction and Enhancement 10

  1.3.4 Real-Time Processing Strategies 11

  1.4 Innovations in Signal Processing for Biomedical Imaging 11

  1.4.1 Machine Learning and AI-Driven Techniques 12

  1.4.2 Quantum Signal Processing in Imaging 12

  1.4.3 Multimodal Imaging and Data Fusion 13

  1.4.4 Emerging Trends in Signal Processing Hardware 13

  1.5 Case Studies 14

  1.5.1 Innovations in Signal Processing for MRI 14

  1.5.2 Deep Learning in Ultrasound Imaging 15

  1.5.3 Hybrid Imaging Modalities 16

  1.6 Challenges and Future Directions 17

  1.6.1 Ethical and Regulatory Concerns 17

  1.6.2 Scalability and Cost Effectiveness of Signal Processing Techniques 18

  1.6.3 Future Trends in Biomedical Signal Processing 18

  1.6.3.1 Image Systems at the Crossroads of Edge AI and IoT 18

  1.6.3.2 Signal Processing for Personalized Imaging 19

  1.7 Advancements in Signal Processing Techniques and Innovations 19

  1.7.1 Future Perspectives on Biomedical Imaging 20

  1.8 Conclusion 21

  References 22

  2 Deep Learning Techniques for Biomedical Imaging 25
  Vandana and Chetna Sharma

  2.1 Introduction 25

  2.2 Overview of DL Architecture in Biomedical Imaging 26

  2.3 CNN Architecture 28

  2.4 Basic Concepts in Biomedical Imaging 29

  2.4.1 Data Representation in Imaging 29

  2.4.2 Image Reconstruction with dl 29

  2.4.2.1 Concept of Image Reconstruction 30

  2.4.3 Image Segmentation 31

  2.4.3.1 Traditional Image Segmentation Techniques 31

  2.4.3.2 dl Image Segmentation Models 32

  2.4.4 Image Registration 32

  2.4.5 Diagnosis and Classification 33

  2.4.5.1 Types of Image Classification 33

  2.4.5.2 Working of Image Classification 34

  2.4.6 Functional and Molecular Imaging 36

  2.4.7 Explainability and Interpretability 37

  2.4.7.1 Significance of Interpretability and Explainability 37

  2.5 Future Study and Application of Image Processing in Biomedical 38

  2.6 Conclusion 39

  References 39

  3 Advanced Methods and Approaches in Image Reconstruction 45
  Navneet Kaur and Gurbinder Singh Brar

  3.1 Introduction 45

  3.1.1 Fundamental Principles of Image Reconstruction 47

  3.1.2 Forward and Inverse Problems in Image Reconstruction 47

  3.1.2.1 Forward Problems 47

  3.1.2.2 Inverse Problems 48

  3.2 Classical Analytical Methods 49

  3.2.1 Filtered Back Projection (FBP) 49

  3.2.2 Fourier-Based Methods 50

  3.2.3 Algebraic and Iterative Techniques 51

  3.2.3.1 Algebraic Reconstruction Techniques (ARTs) 51

  3.2.3.2 Simultaneous Algebraic Reconstruction Technique (SART) 51

  3.3 Convergence and Computational Challenges 52

  3.4 Signal Processing for Noise and Artifact Management 52

  3.4.1 Sources of Noise and Artifacts 54

  3.4.2 Sources of Noise 55

  3.4.3 Sources of Artifacts 57

  3.5 Denoising Techniques 59

  3.5.1 Spatial Domain Filtering 59

  3.5.2 Transform Domain Approaches 59

  3.6 Artifact Correction Methods 60

  3.6.1 Model-Based Correction Techniques 60

  3.6.2 Deep Learning Approaches for Artifact Reduction 61

  3.6.3 Advanced Signal Processing Strategies 61

  3.7 Compressed Sensing in Imaging 62

  3.7.1 Sparse Representation and Sampling 62

  3.7.2 Applications in MRI and CT 62

  3.7.3 Model-Based Reconstruction Techniques 63

  3.7.4 Bayesian Inference Models 63

  3.8 Statistical Methods for Noise Modeling 64

  3.8.1 Machine Learning and Neural Networks 64

  3.8.2 Supervised vs Unsupervised Approaches 64

  3.8.3 Deep Learning for Artifact Removal and Reconstruction 64

  3.8.4 Emerging Innovations in Image Reconstruction 65

  3.9 Hybrid Computational Methods 65

  3.9.1 Optimization-Based Deep Networks 66

  3.9.2 Multimodal and Multiresolution Techniques 66

  3.9.3 Super-Resolution Approaches for Enhanced Detail 67

  3.10 Quantum Signal Processing 68

  3.10.1 Quantum Imaging and Sensing 68

  3.11 AI-Assisted Real-Time Reconstruction 69

  3.12 Conclusion 70

  References 71

  4 Integrative Approaches in Image Analysis and Signal Interpretation 75
  Tanishq Soni, Deepali Gupta, and Mudita Uppal

  4.1 Introduction 75

  4.2 Related Work 78

  4.3 Materials and Methodology 81

  4.3.1 Description of Dataset 81

  4.3.2 Proposed Methodology 82

  4.3.2.1 Input Dataset and Pre-Processing 82

  4.3.2.2 Designing of Deep Learning Models 84

  4.4 Results and Discussion 88

  4.4.1 Analysis Based on Confusion Matrix 88

  4.4.2 Analysis Based on Accuracy 88

  4.4.3 Analysis Based on Loss 88

  4.5 Conclusion and Future Scope 93

  References 93

  5 Multimodal Imaging: Combining Molecular and Optical Approaches 97
  Haewon Byeon, Azzah AlGhamdi, Ismail Keshta, Mukesh Soni, Mohammad Shabaz, and Mohammed Wasim Bhatt

  5.1 Introduction 97

  5.2 Network Model 100

  5.2.1 Dataset Selection 103

  5.2.2 Image Patches for Classification and Regression Localization 104

  5.2.3 Candidate Block Screening Network 106

  5.2.4 Verification Module-Task-Guided Radial Basis Network 107

  5.2.5 Loss Function 110

  5.3 Evaluation and Results from Experiments 111

  5.3.1 Experimental Setting 111

  5.3.2 Performance Evaluation Metrics 112

  5.3.3 The Impact of Picture Block Size on the Efficiency of the Model 112

  5.3.4 The Impact of Deep Supervision and Attention…