Cloud Computing in Medical Imaging

Description:

Today’s healthcare organizations must focus on a lot more than just the health of their clients. The infrastructure it takes to support clinical-care delivery continues to expand, with information technology being one of the most significant contributors to that growth. As companies have become more dependent on technology for their clinical, administrative, and financial functions, their IT departments and expenditures have had to scale quickly to keep up. However, as technology demands have increased, so have the options for reliable infrastructure for IT applications and data storage. The one that has taken center stage over the past few years is cloud computing. Healthcare researchers are moving their efforts to the cloud because they need adequate resources to process, store, exchange, and use large quantities of medical data.

Cloud Computing in Medical Imaging covers the state-of-the-art techniques for cloud computing in medical imaging, healthcare technologies, and services. The book focuses on

Machine-learning algorithms for health data security

Fog computing in IoT-based health care

Medical imaging and healthcare applications using fog IoT networks

Diagnostic imaging and associated services
Image steganography for medical informatics
This book aims to help advance scientific research within the broad field of cloud computing in medical imaging, healthcare technologies, and services. It focuses on major trends and challenges in this area and presents work aimed to identify new techniques and their use in biomedical analysis.

Table of contents :

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Acknowledgements
Preface
Editors
Contributors
Chapter 1. Cloud Computing in Healthcare and Medical Imaging: A Brief Overview
1.1 Introduction
1.2 Adopting Cloud for Healthcare
1.3 Related Work
1.4 Conclusion
References
Chapter 2. ISM and DEMATEL Analysis of H4.0 Enablers in the Indian Healthcare Industry
2.1 Introduction
2.2 Review of Literature
2.2.1 Enablers of Health 4.0
2.3 Research Methodology
2.3.1 ISM Methodology
2.3.2 DEMATEL Methodology
2.4 Modeling the Enablers of the Indian Healthcare Industry by ISM Methodology
2.5 MICMAC Analysis of the Enablers
2.6 DEMATEL Analysis of the Enablers
2.7 Discussion and Conclusion
2.8 Implications of Research
2.9 Limitations and Future Prospects of Research
References
Chapter 3. Machine Learning Algorithms for Health Data Security: A Systematic Review
3.1 Introduction
3.2 Value of Fuels and Lignocellulose as Raw Material
3.3 Research Question
3.3.1 Search and Selection Strategy
3.3.2 Selection Criteria
3.3.3 Thermochemical Routes for Biomass Conversion to Fuels
3.3.4 Observation
3.4 Findings
3.4.1 Systematic Search Results
3.5 Motivation and Algorithms
3.5.1 Health Data Security Using ML and DL Strategy
3.5.2 Health Data Security Using Traditional Approach
3.5.3 Mathematical Interpretation of the ML Models
3.5.4 Support Vector Machine (SVM)
3.5.5 Radial Basis Function (RBF) Kernel SVM
3.5.6 Decision Tree (DT)
3.5.7 Naïve Bayes Algorithm
3.5.8 Complexity Analysis of the ML Models
3.6 Conclusion and Future Work
References
Appendix 3.A
Chapter 4. Fog Computing in IoT-Based Healthcare Systems
4.1 Introduction
4.2 Concepts in Fog Computing
4.3 Healthcare Systems
4.4 Properties of Fog-Based Computing
4.4.1 Privacy
4.4.2 Energy Efficiency
4.4.3 Bandwidth
4.4.4 Scalability
4.4.5 Dependability
4.5 Fog Computing in IoT-Based Healthcare
4.5.1 Computation Center
4.5.2 Latency and Throughput
4.5.3 Reliability
4.5.4 Security
4.5.5 Automatic Fog Computing
4.5.6 Energy Effectiveness
4.6 Conclusion
References
Chapter 5. Medical Imaging and Healthcare Applications Using 5G
5.1 Introductions: Background and Constraints of Traditional Healthcare Applications
5.2 Opportunities Provided by Cloud Computing and 5G
5.3 Medical Imaging with Cloud and 5G
5.3.1 Medical Image Storage
5.3.2 Medical-Image Processing
5.3.3 Machine-Learning and AI
5.3.4 Visualization and Virtual Reality (VR)/Augmented Reality (AR)
5.4 The Impacts of 5G on Cloud Computing
5.5 5G in Healthcare
5.5.1 5G in Healthcare Applications with High Processing Requirements
5.5.2 Benefit of 5G to Healthcare Applications
5.5.3 An Example Application
5.6 Introduction to 5G Technology
5.7 Software Defined Network in 5G
5.8 5G Architecture
5.8.1 Logical Structure of 5G
5.8.2 Slicing and Integrating with Broadband
5.9 Edge Computing
5.10 Conclusion
References
Chapter 6. The Application of Cloud-Computing Technology to Improve Patients’ Medical History Access to Clinicians for Quality of Care in the Fourth Industrial Revolution
6.1 Introduction and Background
6.2 Cloud-Computing Technology and Healthcare Services
6.3 Problem Statement
6.4 Purpose and Objective of the Study
6.5 Methodology
6.6 Discussion and Recommendations
6.6.1 Proposed Framework
6.7 Concluding Remarks
References
Chapter 7. Diagnostic Imaging and Associated Services: Toward Interoperability and Cloud Computing
7.1 Introduction
7.2 DICOM and DICOMWeb
7.2.1 DICOMWeb Search Service (QIDO-RS – Query Based on ID for DICOM Objects)
7.2.2 DICOMWeb Retrieve Service (WADO-RS – Web Access to DICOM Persistent Objects)
7.2.3 DICOMWeb Storage Service (STOW-RS – STore Over the Web)
7.2.4 DICOMWeb Worklist Service (UPS-RS – Unified Procedure Step)
7.2.5 DICOMWeb Capabilities Service
7.3 Discussion
7.4 Conclusion and Future Work
References
Chapter 8. Health Monitoring Based on the Integrated Offer of Cloud-IoT Sensing Services
8.1 Introduction
8.2 Specific HM Architectural Solutions – An Overview
8.3 Generic Sensing Service Scenario
8.4 Integrated Sensing Service Offer Approach
8.4.1 Main Roles and Business Interactions
8.4.2 Operation Rules
8.5 Service Offer Integrator Specificity
8.5.1 SOI Business Model Specifications
8.5.2 Core Entities of the SOI-MIS Data Structure
8.6 Implementation Scenarios for Integrated Sensing Service Approach
8.7 Conclusions
References
Chapter 9. Sleep-Stage Identification Using Recurrent Neural Network for ECG Wearable-Sensor System
9.1 Introduction
9.2 Proposed Methods
9.2.1 Data Collection of MIT-BIH Polysomnographic Dataset
9.2.2 Pre-Processing of the Data
9.2.3 First Algorithm: Long Short-Term Memory
9.2.4 Second Algorithm: LSTM with Peephole Connection
9.2.5 Third Algorithm: Gated Recurrent Unit
9.3 Results
9.3.1 Model Training and Evaluation
9.3.2 Pre-Processing
9.3.3 First Algorithm: Long Short-Term Memory
9.3.4 Second Algorithm: LSTM with Peephole Connection
9.3.5 Third Algorithm: Gated Recurrent Unit
9.4 Conclusion
Chapter 10. Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review
10.1 Introduction
10.1.1 Remote Patient-Monitoring Application Architecture
10.1.2 Alarms in RPM
10.1.3 False-Positive Alarms
10.2 A Pentagon Approach
10.2.1 Clinical Requirements
10.2.2 Method Selection
10.2.3 Design and Development
10.2.4 Clinical Trial and Analysis
10.2.5 Feedback Loop
10.3 Classification Categories
10.3.1 Physiological Data-Based Approach
10.3.1.1 Customised Alarm Signals
10.3.1.2 Machine Learning
10.3.1.3 Time Delay
10.3.1.4 Integration of Techniques
10.3.1.5 Coalition Game Theory
10.3.1.6 Sensor Fusion
10.3.1.7 Pattern Discovery
10.3.2 Clinical Device-Centric Approach
10.3.3 Clinical Knowledge-Based Approach
10.3.3.1 Pattern Match
10.3.3.2 Team-Based Method
10.3.4 Clinical Environment-Based Approaches
10.3.4.1 Median Filters
10.3.4.2 Dimension Reduction
10.4 Results and Discussion
10.4.1 Physiological Data
10.4.2 Clinical Device-Centric
10.4.3 Clinical Knowledge-Based
10.4.4 Clinical Environment
10.5 Conclusions
Notes
References
Chapter 11. Cloud Computing in Medical Imaging, Healthcare Technologies, and Services
11.1 Introduction to Cloud Computing
11.1.1 Essential Characteristics
11.1.2 Service Models
11.1.3 Deployment Models
11.1.4 Introduction to Cloud Computing in Healthcare
11.2 The Benefits of Cloud Computing in the Healthcare Domain
11.3 Looking at the Past of Cloud Computing in Healthcare
11.4 The Present Trends in Cloud-Computing Dynamics
11.5 Analysis
11.5.1 Comparison
11.5.2 How Can It Move Forward?
11.6 Conclusion and Future Scope
References
Chapter 12. An Insight into Image Steganography for Medical Informatics
12.1 Introduction
12.2 Medical Image Steganography
12.3 Classification of Image Steganography Based on Embedding Domain
12.3.1 Spatial Domain Steganography
12.3.2 Transform Domain Steganography
12.3.3 Hybrid Domain
12.3.4 Steganography Based on AI Approaches
12.4 Image Steganography with Encryption
12.5 Spread-Spectrum (SS)-Based Image Steganography
12.6 Performance Evaluation
12.7 Statistical and Analytical Measures Used
12.8 Conclusion
Funding
References
Index