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E-raamat: Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

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  • Ilmumisaeg: 30-Nov-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128160879
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Machine Learning in Bio-Signal Analysis and Diagnostic ImagingSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 30-Nov-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128160879
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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.

The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.

  • Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging
  • Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining
  • Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains
Contributors xiii
Preface xv
Chapter 1 Ontology-Based Process for Unstructured Medical Report Mapping 1(18)
Jefferson Tales Oliva
Huei Diana Lee
Newton Spolaor
Claudio Saddy Rodrigues Coy
Joao Jose Fagundes
Maria de Lourdes Setsuko Ayrizono
Feng Chung Wu
1 Introduction
1(1)
2 Related Work
2(2)
3 Ontology-Based Medical Report Mapping Process
4(6)
3.1 First OMRMP Phase
4(3)
3.2 Second OMRMP Phase
7(2)
3.3 Computational System
9(1)
4 Experimental Setup
10(2)
5 Results and Discussion
12(3)
6 Conclusion
15(1)
7 Relation of the
Chapter With the Book
15(1)
References
16(3)
Chapter 2 A Computer-Aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images 19(34)
Eman A. Abdel Maksoud
Mai Ramadan
Sherif Barakat
Mohammed Elmogy
1 Introduction
19(2)
2 Human Eye Anatomy and Diabetic Retinopathy
21(2)
2.1 Human Eye Anatomy
21(1)
2.2 DR Disease
22(1)
3 The Related Work
23(9)
3.1 The Supervised Methods
23(3)
3.2 Unsupervised Methods
26(1)
3.3 Semiautomated Methods
27(1)
3.4 Combining Structure and Color Features
27(2)
3.5 The Related Work Results and Discussions
29(3)
4 The Proposed Multilabel CAD System
32(9)
4.1 Phase 1: Color Fundus Image Acquisition
32(2)
4.2 Phase 2: Preprocessing
34(1)
4.3 Phase 3: Blood Vessels Segmentation
34(1)
4.4 Phase 4: Feature Extraction
35(1)
4.5 Phase 5: Feature Selection
35(1)
4.6 Phase 6: Classification
36(5)
4.7 Phase 7: The Evaluation
41(1)
5 The Experimental Results
41(5)
5.1 The Methods and Materials
42(1)
5.2 The Results
42(4)
6 The Discussion
46(4)
6.1 The Comparison Between the Presented Methodology and the Others in the Literature Using the Same Dataset
48(2)
7 Conclusion
50(1)
References
50(2)
Further Reading
52(1)
Chapter 3 A DEFS Based System for Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using Ultrasound Images 53(20)
M.B. Subramanya
Jitendra Virmani
Kriti
1 Introduction
53(2)
2 Data Set Description
55(1)
2.1 Clinically Acquired Image Database
55(1)
2.2 ROI Selection Protocol and Data Set Distribution
55(1)
3 Methodology Adopted for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver
56(5)
3.1 Feature Extraction Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver
58(1)
3.2 Feature Selection Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver
58(2)
3.3 Feature Classification Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver
60(1)
4 Experiments and Results
61(6)
4.1 Experiment 1: Differential Diagnosis Between Fatty Liver and Cirrhotic Liver Without Using Feature Selection
61(1)
4.2 Experiment 2: Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using kNN-DEFS
62(4)
4.3 Experiment 3: Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using NB-DEFS
66(1)
5 Discussion
67(2)
6 Conclusion and Future Scope
69(1)
Acknowledgments
70(1)
References
70(3)
Chapter 4 Infrared Thermography and Soft Computing for Diabetic Foot Assessment 73(26)
Sudha Bandalakunta Gururajarao
Umadevi Venkatappa
Joshi Manisha Shivaram
Mohamed Yacin Sikkandar
Abdullah Al Amoudi
1 Introduction
73(1)
2 Characteristics of Thermal Infrared Images
74(2)
2.1 Spatial Characteristics (Resolution)
74(1)
2.2 Noise (Thermal Resolution)
75(1)
2.3 Spectral Characteristics
75(1)
2.4 Dynamic Range
75(1)
3 Medical Infrared Thermography
76(2)
3.1 Early Diagnosis Using Medical Infrared Thermography
76(1)
3.2 How Is IR Thermal Imaging Different From Other Medical Imaging Modalities?
77(1)
3.3 Role of Soft Computing in Medical Infrared Thermography
78(1)
4 Main Focus and Motivation Behind the
Chapter
78(1)
5 Literature Review on Diabetic Foot Complications Assessment Using MIT
79(9)
5.1 Methodology
80(1)
5.2 Study Population
81(1)
5.3 Thermal Image Acquisition and Segmentation
81(2)
5.4 Thermal Image Registration
83(1)
5.5 Extraction of Region of Interest (ROI)
83(1)
5.6 Feature Extraction and Detection of Abnormality
83(2)
5.7 Statistical Analysis
85(1)
5.8 Classification of Foot for the Assessment of Diabetic Complication Using Deep Learning Neural Network
86(2)
6 Challenges for Medical Infrared Thermography
88(2)
6.1 Thermal Image Acquisition
88(1)
6.2 Environmental, Individual, and Technical Challenges
89(1)
6.3 Hardware Requirements
89(1)
6.4 Specific Challenges to Thermal Imaging
89(1)
7 Future Roadmap for MIT and Soft Computing
90(1)
7.1 Issues to be Addressed
90(1)
8 Results and Discussion
91(3)
8.1 Segmentation
91(1)
8.2 Statistical Analysis of the Surface Temperature Distribution (STD) to Detect Abnormality
92(1)
8.3 Classification of Foot Using Transfer Learning of Pre-trained CNN Model
93(1)
9 Future Research Directions on Diabetic Foot Assessment
94(1)
10 Conclusion
94(1)
Acknowledgments
95(1)
References
95(4)
Chapter 5 Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN, and SVM Classifiers Using HRV Analysis 99(28)
M.G. Poddar
Anjali C. Birajdar
Jitendra Virmani
Kriti
1 Introduction
99(1)
2 Materials and Methods
100(4)
2.1 Data Collection and Processing
101(1)
2.2 HRV Analysis
102(1)
2.3 Classification Module
103(1)
3 Results and Discussion
104(10)
4 Conclusion
114(8)
References
122(5)
Chapter 6 Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using Digitized Screen Film Mammograms 127(32)
Indrajeet Kumar
Jitendra Virmani
H.S. Bhadauria
1 Introduction
127(6)
2 Materials and Methods
133(9)
2.1 Description of Image Dataset
133(1)
2.2 Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using Digitized Screen Film Mammograms
133(8)
2.3 Classification Phase
141(1)
3 Experiments and Results
142(9)
3.1 Experiment 1: Experiment Carried Out for the Selection of Optimum ROI Size for the Development of Computer Assisted Framework for 4-Class Breast Tissue Pattern Characterization Using Digitized SFMs
143(2)
3.2 Experiment 2: Experiment Carried Out for the Selection of Optimum ROI Size for the Development of Computer Assisted Framework for 4-Class Breast Tissue Pattern Characterization Using Digitized SFMs
145(2)
3.3 Statistical Analysis
147(1)
3.4 Comparative Analysis
148(1)
3.5 Application of the Proposed Work
148(3)
4 Conclusion and Future Scope
151(3)
4.1 Conclusion
151(1)
4.2 Future Scope
152(2)
References
154(3)
Further Reading
157(2)
Chapter 7 Optimization of ANN Architecture: A Review on Nature-Inspired Techniques 159(24)
Tarun Kumar Gupta
Khalid Raza
1 Introduction
159(1)
2 Artificial Neural Network
160(4)
2.1 Feedforward Neural Network
161(3)
2.2 Recurrent or Feedback Neural Network
164(1)
3 Nature Inspired Algorithms
164(3)
4 Optimization of FNN
167(9)
4.1 Nonnature Inspired Algorithm
167(3)
4.2 Nature Inspired Algorithms
170(6)
5 Discussion and Conclusion
176(2)
References
178(5)
Chapter 8 Ensemble Learning Approach to Motor Imagery EEG Signal Classification 183(26)
Rajdeep Chatterjee
Ankita Datta
Debarshi Kumar Sanyal
1 Introduction
183(4)
1.1 Human Brain
184(1)
1.2 Action Potential
184(1)
1.3 Brain Rhythms
185(1)
1.4 Electroencephalography
185(2)
1.5 Motor Imagery
187(1)
2 Scope and Relevance
187(1)
3 Theoretical Background
187(7)
3.1 Preprocessing
188(1)
3.2 Feature Extraction
188(2)
3.3 Classification
190(4)
3.4 Background Study
194(1)
4 Experimental Preparation
194(12)
4.1 Dataset Description
196(1)
4.2 Experiment I (Exp-I)
197(1)
4.3 Experiment II (Exp-II)
197(2)
4.4 Experiment III (Exp-III)
199(1)
4.5 Experiment IV (Exp-IV)
200(6)
5 Conclusion
206(1)
References
206(3)
Chapter 9 Medical Images Analysis Based on Multilabel Classification 209(38)
Eman A. Abdel Maksoud
Sherif Barakat
Mohammed Elmogy
1 Introduction
209(2)
2 Literature Review
211(21)
2.1 Algorithm Adaptation (Direct) Methods
211(5)
2.2 Problem Transformation (Indirect) Methods
216(2)
2.3 The Hybrid Between Multilabel Classification Methods
218(1)
2.4 Literature Results Analysis and Discussion
219(9)
2.5 Medical Image Analysis via Multilabel Classification
228(4)
3 Multilabel CAD System Framework
232(7)
3.1 Image Acquisition
235(1)
3.2 Preprocessing
235(1)
3.3 Feature Extraction
236(1)
3.4 Feature Selection
237(1)
3.5 Classification
237(2)
3.6 Evaluation
239(1)
4 Challenges of Multilabel Classification
239(3)
4.1 High Dimensionality of Data
241(1)
4.2 Label Dependency
241(1)
4.3 Label Locality
242(1)
4.4 Interlabel Similarity
242(1)
4.5 Interlabel Diversity
242(1)
4.6 The Nature of Multilabel Datasets
242(1)
4.7 Scalability
242(1)
5 Conclusion
242(1)
References
243(2)
Further Reading
245(2)
Chapter 10 Figure Retrieval From Biomedical Literature: An Overview of Techniques, Tools, and Challenges 247(26)
Debarshi Kumar Sanyal
Samiran Chattopadhyay
Rajdeep Chatterjee
1 Introduction
247(3)
2 Contextualization and
Chapter Organization
250(1)
3 Image Retrieval: Basic Concepts
250(3)
3.1 Content-Based Image Retrieval
250(3)
4 Figure Retrieval From Biomedical Papers: Problem Setting
253(1)
5 Figure Retrieval From Biomedical Papers: Design Aspects
253(7)
5.1 Extraction of Figures and Figure Metadata From Research Papers
254(3)
5.2 Building Figure Representation
257(2)
5.3 Indexing of Figures
259(1)
5.4 Query Processing
260(1)
6 Some Figure Search Engines in Biomedical Domain
260(7)
6.1 GoldMiner
261(1)
6.2 FigureSearch
261(3)
6.3 Yale Image Finder
264(1)
6.4 Open-i
264(3)
7 Future Directions
267(1)
8 Conclusion
268(1)
Acknowledgments
268(1)
References
268(5)
Chapter 11 Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images 273(20)
Rohit Thanki
Surekha Borra
1 Introduction
273(1)
2 Machine Learning
274(5)
2.1 Support Vector Machines
274(2)
2.2 Support Vector Regression
276(2)
2.3 Neural Networks
278(1)
3 Application of ML Algorithms in Medical Science
279(9)
3.1 Diagnostic Image Classification Using ML Algorithms
279(2)
3.2 Diagnostic Image Security Using Watermarking With ML Algorithms
281(4)
3.3 Diagnostic Image Security Using Watermarking With Deep Learning Algorithms
285(3)
4 Discussion and Future Work
288(1)
5 Conclusion
289(1)
References
290(3)
Chapter 12 Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective 293(26)
Sitaramanjaneya Reddy Guntur
Rajani Reddy Gorrepati
Vijaya R. Dirisala
1 Introduction
293(2)
2 Overview of IoMRT
295(3)
2.1 Light Fidelity (Li-Fi) System
297(1)
3 Architecture IoMRT
298(5)
3.1 Sensor/Actuator Layer
298(2)
3.2 Network Layer
300(1)
3.3 IoMRT Infrastructure Layer
301(1)
3.4 Application Layer
302(1)
4 Li-Fi Technology Connect to IoMRT for Robotic Surgery
303(1)
5 IoMRT for Robotic Surgery
304(1)
6 Methodology and Analysis Proposed Robotic Arm for Surgery
305(3)
6.1 Hardware Description
308(1)
6.2 Software Description
308(1)
7 Experimental Evaluation
308(3)
7.1 Flow Diagram
308(2)
7.2 Experimental Analysis
310(1)
8 Limitations and Research Challenges
311(2)
8.1 Computational Problem
312(1)
8.2 Optimization
312(1)
8.3 Security Concerns of IoMRT
312(1)
8.4 Ethical Issue
313(1)
9 Advantage and Disadvantages of Robotic Surgery With Other Surgeries
313(1)
10 Applications of Robotics in Healthcare Paradigm
314(1)
11 Conclusions and Future Enhancement
315(1)
References
315(4)
Index 319
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.

He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.

Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published 1 edited book, 8 book chapters and 22 research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a guest editor for 2 journals and reviewer for journals published by IEEE, IET, Elsevier, Taylor & Francis, Springer, IGI-Global etc,. She has received Woman Achiever's Award from The Institution of Engineers (India), for her prominent research and innovative contribution (s)., Woman Educator & Scholar Award for her contributions to teaching and scholarly activities, Young Woman Achiever Award for her contribution in Copyright Protection of Images. Amira S. Ashour is an Assistant Professor and Head of Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Egypt. She is a member in the Research and Development Unit, Faculty of Engineering, Tanta University, Egypt. She received the B.Eng. degree in Electrical Engineering from Faculty of Engineering, Tanta University, Egypt in 1997, M.Sc. in Image Processing in 2001 and Ph.D. in Smart Antenna in 2005 from Faculty of Engineering, Tanta University, Egypt. Ashour has been the Vice Chair of Computer Engineering Department, Computers and Information Technology College, Taif University, KSA for one year from 2015. She has been the vice chair of CS department, CIT college, Taif University, KSA for 5 years. Her research interests are Smart antenna, Direction of arrival estimation, Targets tracking, Image processing, Medical imaging, Machine learning, Biomedical Systems, Pattern recognition, Image analysis, Computer vision, Computer-aided detection and diagnosis systems, Optimization, and Neutrosophic theory. She has 15 books and about 150 published journal papers. She is an Editor-in-Chief for the International Journal of Synthetic Emotions (IJSE), IGI Global, US. Fuqian Shi (Senior Member, IEEE) received the Ph.D. degree in engineering from the College of Computer Science and Technology, Zhejiang University. He was a Visiting Associate Professor with the Department of Industrial Engineering and Management System, University of Central Florida, Orlando, FL, USA, from 2012 to 2014. He is currently an Associate Professor with the Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. He serves more than 30 committee board memberships for international conferences. He has published more than 80 journal articles and conference proceedings. His research interests include fuzzy inference system, artificial neuro networks, and biomechanical engineering. He also serves as an Associate Editor for the International Journal of Ambient Computing and Intelligence (IJACI), the International Journal of Rough Sets and Data Analysis (IJRSDA), and a Special Issue Editor of fuzzy engineering and intelligent transportation in Information: An International Interdisciplinary Journal
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