Hybrid Image Processing Methods for Medical Image Examination

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

Chapter 1: Introduction Introduction to Disease Screening Screening for blood level infection Screening for skin melanoma Stomach ulcer screening Screening for breast abnormality Screening for brain abnormality Screening for the fetal growth Screening for retinal abnormality Screening for lung abnormality Heart disease screening Osteoporosis Screening of COVID-19 infection Medical Image Recording Procedures Summary References Chapter 2: Image Examination Clinical Image Enhancement Techniques Importance of Image Enhancement Introduction to Enhancement Techniques Artefact removal Noise removal Contrast enrichment Edge detection Restoration Colour space correction Image edge smoothening Recent Advancements Hybrid image examination technique Need for multi-level thresholding Thresholding Implementation and evaluation of thresholding process Summary References Chapter 3: Image Thresholding Need for Thresholding of Medical Images Bi-level and Multi-level threshold Common Thresholding Methods Thresholding for Grey Scale and RGB Images 3.4.1 Thresholding with Between-Class Variance 3.4.2 Thresholding with Entropy Functions Choice of Threshold Scheme Performance Issues Evaluation and Confirmation of Thresholding Technique Thresholding Methods Restrictions in Traditional Threshold Selection Process Need for Heuristic Algorithm Selection of Heuristic Algorithm Particle Swarm Optimization Bacterial Foraging Optimization Firefly Algorithm Bat Algorithm Cuckoo Search Social Group Optimization Teaching-Learning-Based Optimization Jaya Algorithm Introduction to Implementation Monitoring Parameter Objective function Single and Multiple Objective function Summary References Chapter 4: Image Segmentation Requirement of Image Segmentation Extraction of Image Regions with Segmentation Morphological approach Circle Detection Watershed algorithm Seed Region Growing Principal Component Analysis Local Binary Pattern Graph Cut approach Contour Based Approach CNN based segmentation Assessment and Validation of Segmentation Construction of Confusion Matrix Summary References Chapter 5: Medical Image Processing with Hybrid Image Processing Method 5.1 Introduction Context Methodology Database Thresholding Otsu’s function Brain Storm Optimization Segmentation Performance evaluation and validation Results and Discussion Summary References Chapter 6: Deep Learning for Medical Image Processing Introduction Implementation of CNN for image assessment Transfer learning concepts AlexNet VGG-16 VGG-19 Medical Image Examination with Deep-Learning: Case study Brain abnormality detection Lung abnormality detection Retinal abnormality detection COVID-19 lesion detection Summary References Chapter 7: Conclusion

Venkatesan Rajinikanth is a Professor in Department of Electronics and Instrumentation Engineering at St. Joseph’s 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