Medical Information Processing and Security: Techniques and applications

Contents
About the Editors
Foreword
Preface
1 Introduction to medical information processing and security: techniques and applications
References
2 Prediction of complications in spine surgery using machine learning: a Health 4.0 study on National Surgical Quality Improvement Program beyond logistic regression model
2.1 Introduction
2.2 Methods
2.2.1 Feature selection
2.2.2 Data preparation
2.3 Results
2.4 Discussion
2.5 Conclusion and future works
References
3 Recent trends in histopathological image analysis
3.1 Introduction
3.2 Why computer-assisted diagnosis for cancer diagnosis?
3.3 Breast cancer and CAD in cancer histopathology
3.4 Challenges
3.5 Datasets
3.6 AI approaches: a bridge between manual and automated intelligent histopathological image analysis
3.6.1 ML-based approaches
3.6.2 DL-based approaches
3.7 Technical paradigm-shifting from automated healthcare services to secure automated healthcare services
3.8 Future directions
3.9 Conclusion
References
4 Terahertz imaging in healthcare
4.1 Introduction
4.2 Comparative study of medical imaging techniques
4.3 THz antennas for healthcare
4.4 THz-TDS for medical imaging
4.5 ML techniques for THz image analysis
4.6 Limitations to THz biomedical imaging application technology
4.7 Future scope and recommendations
References
5 The current state of summarization and visualization in Electronic Health Record (EHR) based on EHR interoperability
5.1 Introduction
5.2 Research questions and motivation
5.2.1 Motivation
5.2.2 Challenges
5.3 Reviewing methodology
5.3.1 Input literature
5.3.2 Methodology
5.4 E-health interoperability
5.4.1 E-health standards
5.4.2 Discussion
5.4.3 Semantic interoperability: IoT-based ontologies
5.4.4 Discussion of IoT-based ontologies
5.5 EHR summarization
5.5.1 EHR definition
5.5.2 EHR summarization approaches
5.5.3 Discussion
5.6 EHR visualization
5.6.1 EHR visualization definition
5.6.2 EHR visualization approaches
5.6.3 Discussion
5.7 Conclusion
References
6 EEG signal classification using robust energy-based least squares projection twin support vector machines
6.1 Introduction
6.2 Related work
6.2.1 Twin support vector machines
6.2.2 Least squares twin support vector machines
6.2.3 Robust energy-based least squares twin support vector machines
6.3 Robust energy-based least squares projection twin support vector machines
6.3.1 Linear RELSPTSVM
6.3.2 RELSPTSVM for multiple projection directions
6.3.3 Non-linear RELSPTSVM
6.4 Experimental results
6.4.1 Classification of EEG signals
6.4.2 Evaluation of the models with UCI datasets
6.4.3 Statistical tests
6.5 Conclusion
Acknowledgements
References
7 Clustering-based medical image segmentation: a survey
7.1 Introduction
7.2 Medical imaging modalities and challenges with segmentation
7.3 FCM and its various variants
7.3.1 FCM clustering algorithm
7.3.2 Fuzzy local information c-means
7.3.3 Multidimensional FCM
7.3.4 Weighted image patch-based FCM
7.3.5 Kernel weighted fuzzy local information c-means
7.3.6 Strong FCM
7.3.7 Neighborhood-weighted FCM clustering
7.3.8 Morphological pyramid with FCM clustering
7.3.9 Malleable fuzzy local median c-means algorithm
7.3.10 Fuzzy algorithm for peak detection, spatial information, and reallocation
7.3.11 Fuzzy clustering algorithm with nonlocal information
7.3.12 Intuitionistic fuzzy sets-based credibility FCM clustering algorithm
7.3.13 Intuitionistic possibilistic FCM clustering algorithm
7.3.14 Internet of things-based predictive modeling for predicting lung cancer using FCM clustering (IoTPMFCM)
7.3.15 Super-pixel FCM clustering (SPFCM)
7.3.16 Multitask FCM clustering
7.3.17 Patch-weighted distance and fuzzy clustering-based image segmentation
7.3.18 Fuzzy local intensity clustering model
7.3.19 Improved fuzzy clustering for image segmentation based on a low-rank prior
7.3.20 A novel kernelized total Bregman divergence-driven possibilistic fuzzy clustering with multiple information constraints for image segmentation
7.4 Conclusion
References
8 Artificial intelligence for genomics: a look into it
8.1 Introduction
8.1.1 Insights into genomics
8.1.2 Data and Human Genome Project
8.1.3 Artificial intelligence
8.2 AI for genomics
8.2.1 AI-based applications for genomics
8.2.2 Protein patterns detection
8.2.3 DNA sequence analysis
8.2.4 Promoter sequence detection
8.2.5 Cancer treatment outcome prediction
8.2.6 Genomics biomarkers
8.2.7 Drug response
8.3 Discussion and conclusions
References
9 Research on security of anonymous communication in wireless healthcare online system
9.1 Introduction
9.2 Privacy-aware PKI model with strong forward security
9.2.1 Strong forward-secure ring signature based on RSA
9.2.2 Privacy-aware PKI model
9.3 Anonymous authentication and key agreement protocols
9.3.1 Dynamic sequence and shared secret-based anonymous identity authentication and key agreement protocol
9.3.2 D-H-based key-sharing protocol
9.4 Trust-based secure directed diffusion routing protocol
9.4.1 Energy trust model
9.4.2 The TSDDR protocol
9.4.3 Performance analysis
9.5 Lightweight anonymous communication model
9.5.1 Anonymous IBE program
9.5.2 Lightweight anonymous communication model based on IBE
9.5.3 Experiments and results
9.5.4 Discussion
9.6 Conclusion
References
10 A comprehensive study on the security of medical information using encryption
10.1 Introduction
10.1.1 Medical information
10.1.2 Information security paradigms
10.2 State-of-the-art encryption approaches
10.2.1 Traditional techniques
10.2.2 Hybrid techniques
10.2.3 Deep learning-based techniques
10.3 Comparative studies
10.3.1 Method universality
10.3.2 Evaluation metrics
10.4 Potential challenges and future scope
10.4.1 Potential challenges
10.4.2 Future scope
10.5 Conclusion
References
11 Electrocardiogram-based dual watermarking scheme for healthcare applications
11.1 Introduction
11.2 Related works
11.3 Preliminary concepts
11.3.1 Redundant discrete wavelet transform
11.3.2 QR decomposition
11.3.3 Fast Walsh Hadamard transform
11.4 The proposed scheme
11.4.1 Pre-processing of the textual form of watermark and the ECG signal
11.4.2 Watermark embedding and extraction
11.5 Experimental results
11.6 Conclusions
References
12 Application of autoencoder in craniofacial reconstruction of forensic medicine
12.1 Introduction
12.2 The principle of AE
12.2.1 Traditional AE
12.3 Improved AE
12.3.1 Improved AE with constraints
12.3.2 Improved AE combined with GAN
12.3.3 Other improved AE
12.4 The application of AE in forensic
12.4.1 The application of AE in face reconstruction
12.4.2 The application of AE in craniofacial reconstruction
12.5 Summary and future work
Acknowledgements
References
13 Security and Privacy in smart Internet of Things environments for well-being in the healthcare industry
13.1 Introduction
13.2 Related work
13.3 Trending technologies in the smart healthcare system
13.3.1 Wireless network technologies
13.3.2 Machine learning and deep learning technologies
13.3.3 Blockchain technologies
13.4 Benefits of a smart healthcare system
13.5 Security challenges in the healthcare system
13.6 Conclusion
References
14 A survey of medical image watermarking: state-of-the-art and research directions
14.1 Introduction
14.2 Survey of transform domain-based watermarking methods
14.2.1 Wavelet transform-based watermarking methods
14.2.2 Curvelet transform-based watermarking methods
14.2.3 Contourlet transform-based watermarking methods
14.2.4 NSCT-based watermarking methods
14.2.5 Other perspectives
14.3 Conclusion
Acknowledgements
References
15 Secure communication and privacy preserving for medical system
15.1 Introduction
15.1.1 Background
15.1.2 Related works
15.1.3 Authentication for the communication between people and devices
15.1.4 Group communication authenticated protocol
15.1.5 Privacy protection for data query
15.1.6 Data aggregation protocol
15.1.7 Raw medical data collection and publishing protocol
15.2 Secure and lightweight authentication protocol
15.2.1 System model
15.2.2 Secure and lightweight authentication protocol
15.2.3 Experiment analysis
15.3 Group communication authenticated protocol
15.3.1 System design
15.3.2 Protocol processes
15.3.3 Experiment analysis
15.4 Obvious transmission protocol in data query
15.4.1 System design
15.4.2 Remote medical system design
15.4.3 Experiment analysis
15.5 Data aggregation protocol in medical data publishing
15.5.1 System model of the protocol
15.5.2 Aggregation steps of the protocol
15.5.3 Experiment analysis of the protocol
15.5.4 System model of the protocol
15.5.5 Aggregation steps of the protocol
15.5.6 Experiment analysis of the protocol
15.6 Raw medical data collection and publishing protocol
15.6.1 System model of the protocol
15.6.2 Raw medical data collection of the protocol
15.6.3 Experiment analysis of the protocol
15.6.4 System model of the protocol
15.6.5 Raw medical data collection of the protocol
15.6.6 Experiment analysis of the protocol
15.7 Conclusion
Reference
16 Secure medical image encryption algorithm for e-healthcare applications
16.1 Introduction
16.2 Literature review
16.3 Preliminaries
16.3.1 Chaotic system
16.3.2 Lempel–Ziv–Welch compression
16.4 Proposed scheme
16.4.1 Generation of chaos initial value
16.4.2 Key generation
16.4.3 Encryption process
16.4.4 Compression and decompression of encrypted image
16.4.5 Decryption process
16.5 Experimental results
16.5.1 Statistical analysis
16.5.2 Differential analysis
16.5.3 Key space analysis
16.5.4 Key sensitivity analysis
16.5.5 Classical attack analysis
16.5.6 Robustness analysis
16.5.7 Computational cost analysis
16.5.8 Compression performance
16.6 Conclusion
References
17 Conclusion and future directions
Index
Back Cover