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E-raamat: Hybrid Image Processing Methods for Medical Image Examination [Taylor & Francis e-raamat]

(St. Josephs College of Eng., Chennai, India), (Sri Sairam Eng. College, Chennai, India), , (University of Houston- Downtown, Houston, TX)
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Hybrid Image Processing Methods for Medical Image ExaminationSuurem pilt
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In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book:











Provides broad background on various image thresholding and segmentation techniques





Discusses information on various assessment metrics and the confusion matrix





Proposes integration of the thresholding technique with the bio-inspired algorithms





Explores case studies including MRI, CT, dermoscopy, and ultrasound images





Includes separate chapters on machine learning and deep learning for medical image processing
Preface ix
Authors xi
Chapter 1 Introduction
1(46)
1.1 Introduction to Disease Screening
2(30)
1.1.1 Screening of Blood Sample
5(4)
1.1.2 Screening for Skin Melanoma
9(2)
1.1.3 Stomach Ulcer Screening
11(1)
1.1.4 Screening for Breast Abnormality
12(5)
1.1.5 Screening for Brain Abnormality
17(7)
1.1.6 Screening for the Fetal Growth
24(1)
1.1.7 Screening for Retinal Abnormality
25(1)
1.1.8 Screening for Lung Abnormality
25(5)
1.1.9 Heart Disease Screening
30(1)
1.1.10 Osteoporosis
31(1)
1.1.11 Screening of COVID-19 Infection
32(1)
1.2 Medical Image Recording Procedures
32(9)
1.3 Summary
41(6)
References
41(6)
Chapter 2 Image Examination
47(22)
2.1 Clinical Image Enhancement Techniques
47(1)
2.2 Importance of Image Enhancement
48(1)
2.3 Introduction to Enhancement Techniques
48(12)
2.3.1 Artifact Removal
48(1)
2.3.2 Noise Removal
49(3)
2.3.3 Contrast Enrichment
52(2)
2.3.4 Edge Detection
54(1)
2.3.5 Restoration
55(1)
2.3.6 Color Space Correction
56(2)
2.3.7 Image Edge Smoothing
58(2)
2.4 Recent Advancements
60(4)
2.4.1 Hybrid Image Examination Technique
60(1)
2.4.2 Need for Multi-Level Thresholding
60(2)
2.4.3 Thresholding
62(1)
2.4.4 Implementation and Evaluation of Thresholding Process
62(2)
2.5 Summary
64(5)
References
65(4)
Chapter 3 Image Thresholding
69(34)
3.1 Need for Thresholding of Medical Images
69(1)
3.2 Bi-Level and Multi-Level Threshold
70(1)
3.3 Common Thresholding Methods
71(1)
3.4 Thresholding for Greyscale and RGB Images
72(7)
3.4.1 Thresholding with Between-Class Variance
73(1)
3.4.2 Thresholding with Entropy Functions
74(5)
3.5 Choice of Threshold Scheme
79(1)
3.6 Performance Issues
80(1)
3.7 Evaluation and Confirmation of Thresholding Technique
80(2)
3.8 Thresholding Methods
82(1)
3.9 Restrictions in Traditional Threshold Selection Process
83(1)
3.10 Need for Heuristic Algorithm
83(2)
3.1 Selection of Heuristic Algorithm
85(11)
3.11.1 Particle Swarm Optimization
86(1)
3.11.2 Bacterial Foraging Optimization
87(3)
3.11.3 Firefly Algorithm
90(1)
3.11.4 Bat Algorithm
91(1)
3.11.5 Cuckoo Search
92(1)
3.11.6 Social Group Optimization
92(2)
3.11.7 Teaching-Learning-Based Optimization
94(2)
3.11.8 Jaya Algorithm
96(1)
3.12 Introduction to Implementation
96(1)
3.13 Monitoring Parameter
97(2)
3.13.1 Objective Function
97(1)
3.13.2 Single and Multiple Objective Function
98(1)
3.14 Summary
99(4)
References
99(4)
Chapter 4 Image Segmentation
103(26)
4.1 Requirement of Image Segmentation
103(1)
4.2 Extraction of Image Regions with Segmentation
104(19)
4.2.1 Morphological Approach
104(2)
4.2.2 Circle Detection
106(3)
4.2.3 Watershed Algorithm
109(1)
4.2.4 Seed Region Growing
109(2)
4.2.5 Principal Component Analysis
111(1)
4.2.6 Local Binary Pattern
111(2)
4.2.7 Graph Cut Approach
113(1)
4.2.8 Contour-Based Approach
114(1)
4.2.9 CNN-Based Segmentation
115(8)
4.3 Assessment and Validation of Segmentation
123(1)
4.4 Construction of Confusion Matrix
124(1)
4.5 Summary
125(4)
References
126(3)
Chapter 5 Medical Image Processing with Hybrid Image Processing Method
129(18)
5.1 Introduction
129(1)
5.2 Context
130(1)
5.3 Methodology
131(6)
5.3.1 Database
131(3)
5.3.2 Thresholding
134(1)
5.3.3 Otsu's Function
134(1)
5.3.4 Brain Storm Optimization
135(1)
5.3.5 Segmentation
135(2)
5.3.6 Performance Evaluation and Validation
137(1)
5.4 Results and Discussion
137(6)
5.5 Summary
143(4)
References
143(4)
Chapter 6 Deep Learning for Medical Image Processing
147(38)
6.1 Introduction
147(2)
6.2 Implementation of CNN for Image Assessment
149(2)
6.3 Transfer Learning Concepts
151(4)
6.3.1 AlexNet
151(2)
6.3.2 VGG-16
153(2)
6.3.3 VGG-19
155(1)
6.4 Medical Image Examination with Deep-Learning: Case Study
155(26)
6.4.1 Brain Abnormality Detection
155(9)
6.4.2 Lung Abnormality Detection
164(10)
6.4.3 Retinal Abnormality Detection
174(1)
6.4.4 COVID-19 Lesion Detection
175(6)
6.5 Summary
181(4)
References
182(3)
Chapter 7 Conclusion
185(2)
Index 187
Venkatesan Rajinikanth is a Professor in Department of Electronics and Instrumentation Engineering at St. Josephs College of Engineering, Chennai 600119, Tamilnadu, India. Recently he edited a book titled Advances in Artificial Intelligence Systems, Nova Science publisher, USA. He has published more than 75 papers. He is the Associate Editor of Int. J. of Rough Sets and Data Analysis (IGI Global, US, DBLP, ACM dl) and Editing/Edited Special Issues in journals, such as Current Signal Transduction Therapy (Bentham Science), Current Medical Imaging Reviews (Bentham Science) and International Journal of Swarm Intelligence Research (IGI Global). He recently published an Indian patent titled Disease Diagnosis System based on Electromyography. His main research interests include Medical Imaging, Machine learning, and Computer Aided Diagnosis. Research Gate: https://www.researchgate.net/profile/Venkatesan_Rajinikanth E. Priya completed her Ph.D at MIT Campus, Anna University in the field of Automated analysis using image processing and artificial intelligence for the diagnosis of tuberculosis images. At present she is a Professor at the Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Affiliated to Anna University, Chennai. She has 17 years of teaching experience, 4 years of research experience and 3 years of industrial experience. She is a recipient of DST-PURSE fellow and a project participant of India-South African collaborative project titled Development of computing tools for decision support in health assessment in rural areas. Her areas of interest include bio-medical imaging, image processing, signal processing, application of artificial intelligence and machine learning techniques. She has published papers in refereed International Journals, Conferences and book chapters in the area of medical imaging and infectious diseases. Research Gate: https://www.researchgate.net/profile/Ebenezer_Priya Hong Lin holds a Ph.D. in Computer Science. His graduate work includes theoretical and empirical studies of parallel programming models and implementations. Dr. Lin has worked on large-scale computational biology at Purdue University, active networks at the National Research Council Canada, and network security at Nokia, Inc. Dr. Lin joined UHD at 2001 and he is currently a professor in computer science. He has worked on parallel computing, multi-agent systems, and affective computing since he joined UHD. He established the Grid Computing Lab at UHD through an NSF MRI grant. He has been a Scholars Academy mentor, an REU faculty mentor, and a CAHSI faculty mentor. His research interests include parallel/distributed computing, data analytics, and human-centered computing. He is a senior member of the ACM. Dr. Lin is the Chair of the inspiring conference series Workshop on Computer Science and Engineering (WCSE). Research Gate: https://www.researchgate.net/profile/Hong_Lin5 Fuhua (Oscar) Lin is a Professor of School of Computing and Information Systems, Athabasca University, Canada. Further, Dr. Lin is also acting as guest professor in Waseda University (Japan) and China Jiliang University (China). Dr. Lin is a senior member in ACM and IEEE. He served as program committee member for various international conferences including the recent conferences, such as EduTrainment (2019), Canadian AI conference (2019), Intelligent Tutoring systems (2019) and General Executive Chair for the 4th IEEE CyberSciTech-2019. Further, he has received various prestigious awards including Outstanding Leadership Award by IEEE in the year 2018. Dr. Lin has published more than 100 papers. His main research interests include Artificial Intelligence, Virtual Reality, e-Learning, Industrial Engineering, and Mathematical Modelling. Research Gate: https://www.researchgate.net/profile/Fuhua_Lin2
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