Image Processing for Automated Diagnosis of Cardiac Diseases

1st Edition - July 13, 2021
Editors: Kalpana Chauhan, Rajeev Kumar Chauhan
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 0 6 4 - 3
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 0 6 5 - 0

Image Processing for Automated Diagnosis of Cardiac Diseases highlights current and emerging technologies for the automated diagnosis of cardiac diseases. It presents concept… Read more

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Image Processing for Automated Diagnosis of Cardiac Diseases highlights current and emerging technologies for the automated diagnosis of cardiac diseases. It presents concepts and practical algorithms, including techniques for the automated diagnosis of organs in motion using image processing.

This book is suitable for biomedical engineering researchers, engineers and scientists in research and development, and clinicians who want to learn more about and develop advanced concepts in image processing to overcome the challenges of automated diagnosis of heart disease.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Acknowledgment
Chapter 1: Cardiac diseases and their diagnosis methods
Abstract
1.1: Introduction
1.2: Heart valves
1.3: Mitral valve regurgitation
1.4: Heart diseases
1.5: Mitral valve diseases
1.6: Cardiac disease diagnosis methods
1.7: Results and analysis
1.8: Discussion
1.9: Conclusions
Chapter 2: Cardiac multimodal image registration using machine learning techniques
Abstract
2.1: Introduction
2.2: Datasets
2.3: Convolutional neural networks for image registration
2.4: Cardiac image registration multimodalities
2.5: Evaluation of multimodal imaging
2.6: Conclusion and discussion
Chapter 3: Anatomical photo representations for cardiac imaging training
Abstract
3.1: Clinical background and motivation
3.2: Technical challenges in multimodal cardiac image analysis and objectives
3.3: Conclusions
Chapter 4: Cardiac function review by machine learning approaches
Abstract
4.1: Cardiac MR and ultrasound image segmentation
4.2: Super-resolution in magnetic resonance images
4.3: Multimodal cardiac image registration
4.4: Machine learning models in image analysis
4.5: Applications of ML models in medical imaging
4.6: Conclusion
Chapter 5: Despeckling in echocardiographic images using a hybrid fuzzy filter
Abstract
Acknowledgment
5.1: Introduction
5.2: Background of despeckle filtering
5.3: Proposed hybrid fuzzy filters (HFFs)
5.4: Experimental results and discussion
5.5: Conclusion
Chapter 6: Impetus to machine learning in cardiac disease diagnosis
Abstract
6.1: Impetus to machine learning in cardiac disease diagnosis
6.2: Introduction to medical imaging
6.3: Role of computers in medical imaging
6.4: Introduction to machine learning
6.5: Impact of machine learning in everyday life
6.6: Applications of machine learning in disease diagnosis
6.7: Machine learning in cardiac disease diagnosis
6.8: Potential challenges of using machine learning in disease diagnosis
6.9: Constraints of using machine learning
6.10: How to develop a machine learning model for the medical domain?
6.11: Validation and performance assessment
6.12: Results and discussions
6.13: Conclusion
Chapter 7: Wavelet transform for cardiac image retrieval
Abstract
7.1: Introduction
7.2: Discrete wavelet transform
7.3: Orthogonal wavelet transform
7.4: Biorthogonal wavelet transform
7.5: Gabor wavelet transform
7.6: Result analysis
7.7: Conclusion
Chapter 8: AI-based diagnosis techniques for cardiac disease analysis and predictions
Abstract
8.1: Introduction
8.2: AI-based cardiac disease diagnosis techniques
8.3: Future of automated diagnosis of cardiac disease
8.4: Cardiovascular disease and COVID-19
8.5: Analysis of electrocardiography
8.6: Results and discussion
8.7: Conclusion and future scope
Chapter 9: An improved regularization and fitting-based segmentation method for echocardiographic images
Abstract
9.1: Introduction
9.2: Materials and method
9.3: Theory and calculation
9.4: Results
9.5: Discussions
9.6: Conclusions
Chapter 10: Identification of heart failure from cine-MRI images using pattern-based features
Abstract
10.1: Introduction
10.2: Pattern-based features
10.3: System overview
10.4: Results and discussion
10.5: Conclusions
Chapter 11: Medical image fusion methods: Review and application in cardiac diagnosis
Abstract
11.1: Introduction
11.2: Cardiac image fusion
11.3: Analysis of fused images
11.4: Results and analysis of fusion
11.5: Conclusions
Index

Kalpana Chauhan

Dr. Kalpana Chauhan (M’13-SM’17) ) graduated in Electrical and Electronics Engineering from The College of Engineering Roorkee, India. She received her M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD in Electrical Engineering from the Indian Institute of Technology Mandi, India. Presently She is working as Associate Professor at the Galgotias College of Engineering and Technology Greater Noida. Dr. Chauhan worked as Dean (Research) at SIRDA Group of Institutions Sundernagar, India (2013-2017). She has also worked as Visiting Scientist in the Center for Electromechnics at the University of Texas at Austin, US. Her special field of interest included DC Micro-grid, Building Automation System, Industrial Automation and Control. She is also the associate member of Institution of Engineers (India), IAENG Hong Kong and ICASIT Singapore.

Affiliations and expertise

Assistant Professor, Department of Electrical Engineering, Central University of Haryana, Mahendargarh, India

Rajeev Kumar Chauhan

Rajeev Kumar Chauhan (M’10-SM’15) graduated in Electrical Engineering from The Institutions of Engineers (India).He received his M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD from School of Computing and Electrical Engineering at Indian Institute of Technology Mandi, India. Presently he is working as Assistant Professor (Senior Grade) at the Galgotias College of Engineering and Technology Greater Noida, India. Dr. Chauhan received POSOCO Power system Award (PPSA-2017) and Best PhD Thesis Award to recognize and reward innovative technical research excellence in power system. He has also received 2nd prize (Category Energy) in IEEE IAS CMD Humanitarian project contest 2017 for his project DC Micro-grid for Residential Buildings. He also received Bhaskara Advanced Solar Energy (BASE-2014) Award, for his research proposal in the area of DC microgrid from Department of Science and Technology, Indo-US Science and Technology Forum.

Affiliations and expertise

Assistant Professor, Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health