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Electronic Imaging Applications in Mobile Healthcare
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Book Description

The integration of mobile technology into healthcare has evolved into an important research area called mHealth. Current mobile imaging applications enhance communication between different healthcare providers, enhance direct interaction between providers and patients, and allow medical images to be referenced from almost any location. This book presents state-of-the-art research in electronic imaging technologies and their applications to mobile healthcare. Twelve chapters by leaders in their fields are divided into four parts: Part I on image processing and enhanced visualization; Part II on security issues in mobile healthcare applications; Part III on human external pulsometers and activity recognition; and Part IV on mobile healthcare applications, including skin cancer monitoring with an iPhone using image retrieval techniques, a mobile healthcare interface, and an automatic multiview food classification method for food intake assessment on a smart phone. The editors hope that this book will inspire further research in mHealth.


Book Details

Date Published: 29 January 2016
Pages: 388
ISBN: 9781628418729
Volume: PM261

Table of Contents
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Preface
List of Contributors
Acronyms and Abbreviations

I MOBILE HEALTHCARE IMAGE PROCESSING AND ENHANCED VISUALIZATION

1 Introduction to Electronic Imaging Applications in Mobile Healthcare
Zilong Hu
1.1 Introduction
1.2 Electronic Imaging in Mobile Health
1.3 Mobile Device Use in Healthcare
     1.3.1 Some common mobile platforms
     1.3.2 Key techniques used in mobile electronic imaging
1.4 Examples of Mobile Healthcare Applications
1.5 Future Research Directions
     1.5.1 Developing algorithms with lower CPU and power requirements
     1.5.2 GPU accelerated technology for mobile image analysis
References

2 A Mobile Image Enhancement Technology for Low-Vision Patients
Fahao Qiao and Jinshan Tang
2.1 Introduction
2.2 Image Enhancement Algorithm
     2.2.1 2D discrete wavelet transform
     2.2.2 Direct contrast enhancement in the wavelet domain—Tang's algorithm
2.3 Implementation on the Mobile Phone
     2.3.1 Xcode
     2.3.2 Framework of the system
2.4 Experimental Results and Conclusions
     2.4.1 Enhancement effectiveness of Tang's algorithm
     2.4.2 Comparison of enhancement effectiveness of two contrast manipulation schemes
References

3 Cellphone Camera Color Medical Imaging via Fast Fourier Transform
Artyom M. Grigoryan and Sos S. Agaian
3.1 Introduction
3.2 Color Images in the Quaternion Space
3.3 Two-Sided 2D Quaternion DFTs
     3.3.1 Column–row algorithm of the two-sided QDFT
     3.3.2 Fast algorithms for the 2D DQFT
     3.3.3 The right-side 2D QDFT
     3.3.4 Column–row wise calculation of the 2D QDFT
3.4 Color and Quaternion Image Tensor Representation
     3.4.1 2D discrete wavelet transform
3.5 α-Rooting Image Enhancement with 2D QDFT
3.6 Conclusion
References

4 Color Enhancement and Correction for Camera Cell Phone Medical Images Using Quaternion Tools
Artyom M. Grigoryan and Sos S. Agaian
4.1 Introduction
4.2 Quaternion Arithmetic, 2D DFT, and Color Image Processing
     4.2.1 The quaternion numbers
     4.2.2 Quaternions and color images
4.3 Mapping the 2D DFT in Quaternion Algebra
     4.3.1 Two-sided 2D QDFT
4.4 Transform-based Image Enhancement
     4.4.1 Gray-level image enhancement measure
4.5 Quaternion-Transform-based Image Enhancement
     4.5.1 New color image quality measure
     4.5.2 Enhancement of images by colors
4.6 Conclusion
References

5 An Adapted Retinex Algorithm with Complexity Optimization for Mobile Phone Medical Image Enhancement
Artyom M. Grigoryan and Sos S. Agaian
5.1 Introduction
5.2 Retinex Algorithms
     5.2.1 Single-scale retinex method
     5.2.2 Multiscale retinex method
     5.2.3 Multiscale retinex color restoration method
5.3 Fast Fourier Transform Multiscale Retinex
5.4 FFT-based Image Enhancement: α-Rooting
5.5 Script of the FFT-Multiscale Retinex
Conclusion
References

II SECURITY ISSUES IN MOBILE HEALTHCARE APPLICATIONS

6 Secure Medical Image Processing for Mobile Devices using Cloud Services
Xiaohui Yuan and Mahadevan Gomathisankaran
6.1 Introduction
6.2 Background and Related Work
6.3 Homomorphic RNS Encryption
     6.3.1 Semi-perfect secrecy
     6.3.2 Modulus confidentiality
     6.3.3 Montgomery representation variations
     6.3.4 Overflow and sign detection
6.4 Secure Image Processing
6.5 Experimental Results
     6.5.1 Implementation and experimental design
     6.5.2 Results and discussion
6.6 Conclusion
References

7 Cell-Phone Medical Image Encryption Based on a Method of 3D Spirals
Artyom M. Grigoryan and Bryan Wiatrek
7.1 Introduction
     7.1.1 Modern cryptography
7.2 Digital Images and Traditional Modern Image Cryptography
7.3 Nontraditional Modern Image Cryptography
7.4 Image, Tensor Representation, and Fourier Transform
7.5 Decomposition by Directional Images
7.6 Tensor Transform in Image Cryptography
7.7 Image Encryption
     7.7.1 Block diagram of encryption
     7.7.2 Complexity: The number of keys
7.8 Encryption of Color Images
7.9 Conclusions
References

III HUMAN EXTERNAL PULSOMETERS AND ACTIVITY RECOGNITION USING MOBILE DEVICES

8 Human Activity Recognition and Processing for Mobile Applications
Jafet Morales, Sahak Kaghyan, David Akopian, and Sos S. Agaian
8.1 Introduction
8.2 External Environmental and Video Sensors Based Approaches for Activity Classification
8.3 Wearable Sensors and Mobile Devices as an Approach for Activity Classification
     8.3.1 Mobile device positioning technologies used for activity estimation
     8.3.2 Wearable inertial sensors
     8.3.3 Using mobile devices and their sensors for activity classification
     8.3.4 Strengths and weaknesses of mobile sensor based activity classification
     8.3.5 Activity recognition algorithms and platforms for smartphone-based activity recognition
8.4 Splitting Activity Recognition Tasks for Mobile Computing
     8.4.1 Multithreading layered mobile computing
8.5 Advanced Algorithms for Activity Classification by Mobile Devices
     8.5.1 Orientation-invariant motion signals
     8.5.2 Feature extraction
     8.5.3 Classification methods
8.6 Testing of Algorithms
8.7 Concluding Remarks
References

9 An Improved Smartphone Heart Rate Acquisition System
Gevorg Karapetyan, Rafayel Barseghyan, Hakob Sarukhanyan, and Sos S. Agaian
9.1 Introduction
     9.1.1 Background
     9.1.2 Mobile health market
     9.1.3 HR measurement via mobile phone camera with a finger over the camera
9.2 Improved Remote HR Acquisition System
9.3 Experimentation and Validation of the Improved Smartphone HR Acquisition System
     9.3.1 Indoor measurement experiments
     9.3.2 Outdoor measurement experiments
     9.3.3 HR change measurement during anger and happiness via mobile device
9.4 Implementation on Mobile a Device
     9.4.1 Computer simulations
9.5 Concluding Remarks
Acknowledgments
References

IV MOBILE HEALTHCARE APPLICATIONS

10 An iPhone Application for Skin Cancer Monitoring
Yanliang Gu and Jinshan Tang
10.1 Introduction
10.2 Development Environment
10.3 Skin Cancer Image Retrieval Using Boundary Information
     10.3.1 Introduction to image retrieval
     10.3.2 Feature extraction using a Fourier descriptor
     10.3.3 Similarity metric
10.4 System Description
     10.4.1 Flowchart for the iPhone application prototype
     10.4.2 User interface for iPhone application
10.5 Experimental Results
10.6 Conclusion
References

11 A Mobile Healthcare Interface
Wei Hu, Daikun Zou, Kai Zhang, Jun Liu, and Xiaoming Liu
11.1 Introduction
11.2 Background
11.3 System Architecture Model
     11.3.1 System design
11.4 Module Design
     11.4.1 Module design for the doctor's end
     11.4.2 Module design for the nurse's end
     11.4.3 Module design for the data access interface
11.5 Fast Medical Image Processing Approach on a Mobile Device
11.6 Implementation
     11.6.1 Implementation of system functions
11.7 Conclusions
References

12 DietCam: Multiview Regular Shape Food Recognition with a Camera Phone
Fanyu Kong, Hongsheng He, Hollie A. Raynor, and Jindong Tan
12.1 Introduction
12.2 Related Work
12.3 Multiview Food Recognition
     12.3.1 Food features
     12.3.2 Camera calibration
     12.3.3 Perspective distance
     12.3.4 Multiview representation
12.4 Implementation
12.5 Experiment
     12.5.1 Dataset
     12.5.2 Baseline methods
     12.5.3 Segmentation results
     12.5.4 Classification results
12.6 Discussion
12.7 Conclusion
12.8 Conclusion
References

Index

Preface

Information technology is changing healthcare systems in revolutionary ways; there can be no health care reform without an information revolution. One information technology that is transforming healthcare systems is mobile technology. As it develops and matures, mobile technology is having a significant impact on healthcare, and emerging mobile technologies are attracting significant attention as well as investment of time and effort among researchers and industrial developers. The combination of mobile technology with healthcare has produced an important research area called mHealth. In 2011, U.S. Secretary of Health and Human Services, Kathleen Sebelius, referred to mHealth as "the biggest technology breakthrough of our time" and maintained that its use would "address our greatest national challenge." Based on related research, mobile health is projected to be a 26 billion dollar industry by 2017.

Mobile technology has wide-ranging applications in human healthcare, such as monitoring elderly patients, security access control for electronic health records, and remote radiology. The primary drivers behind these applications are varied, as evidenced by the following facts:

● Current mobile computing devices already offer many advanced features, such as high-quality cameras, web searching, sound recording, and global positioning systems (GPS).

● The capabilities of mobile computing devices (mobile tablet devices and smartphones) are growing.

● The implementation of mobile imaging platform/systems is growing. Currently, thousands of apps are available, including apps for disease diagnosis, diet and disease tracking, medication and exercise planning, and blood pressure monitoring.

● A growing number of physicians are recognizing the advantages of using mobile tools.

● The mobile technologies in current use are already providing new opportunities by boosting communication between different healthcare providers and between healthcare providers and patients, and by allowing access to medical images from virtually any location.

In fact, a 2012 study by Manhattan Research discovered that approximately 62% of U.S. doctors utilize some type of tablet device in their practice, nearly doubling the adoption rate since 2011.

According to industry evaluations, 500 million smartphone users worldwide will be using a healthcare application by 2018, and 50% of the more than 3.4 billion smartphone and tablet users will have downloaded mobile health applications. Moreover, the Food and Drug Administration (FDA) "recognizes the extensive variety of actual and potential functions of mobile apps, the rapid pace of innovation in mobile apps, and the potential benefits and risks to public health represented by these apps." Finally, mobile computing devices have become commonplace in healthcare settings, leading to rapid growth in the development of biomedical software applications for these platforms.

The aim of this book is to publish state-of-the-art research in electronic imaging technologies as applied to mobile healthcare, and to promote research in mHealth. The twelve chapters in this book are organized into four parts:

Part I deals with image processing and enhanced visualization. Chapter 1 introduces image processing techniques for mobile healthcare systems. Chapter 2 presents image enhancement technology for low-vision patients who use mobile devices to see images. Chapter 3 describes the application of fast Fourier transform-based methods for color medical imaging in mobile devices. Chapter 4 presents new quaternion-based image enhancement tools that can be used as a preprocessing step in conventional cell phone imaging systems by improving the interpretability of information in images for phone viewers. Chapter 5 develops an adapted retinex algorithm for medical image enhancement using mobile phones.

Part II deals with security issues in mobile healthcare applications. Chapter 6 examines security issues for mobile devices using cloud services and presents a homomorphic encryption method that enables direct operation over the encoded data and hence facilitates complete privacy protection. Chapter 7 proposes a novel and fast encryption of images and their decryption without loss of information for medical image viewing on a cell phone.

Part III covers human external pulsometers and activity recognition using mobile devices. Chapter 8 addresses human activity recognition and processing in mobile environments. Chapter 9 develops mobile applications to measure a person's heart rate using a mobile phone camera.

Part IV includes three chapters on mobile healthcare applications. Chapter 10 deals with skin cancer monitoring with an iPhone using image retrieval techniques. Chapter 11 presents a user interface for mobile healthcare. Finally, Chapter 12 presents an automatic multiview food classification method for a food intake assessment system on a smartphone.

We hope that this book will inspire further research in mHealth.

Jinshan Tang
Sos S. Agaian
Jindong Tan
January 2016


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