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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Contributors - p. iii 6. Preface - p. xiii 7. Acknowledgment - p. xiv 8. Chapter 1. Cardiac diseases and their diagnosis methods - p. 1 9. Chapter 2. Cardiac multimodal image registration using machine learning techniques - p. 21 10. Chapter 3. Anatomical photo representations for cardiac imaging training - p. 35 11. Chapter 4. Cardiac function review by machine learning approaches - p. 49 12. Chapter 5. Despeckling in echocardiographic images using a hybrid fuzzy filter - p. 77 13. Chapter 6. Impetus to machine learning in cardiac disease diagnosis - p. 99 14. Chapter 7. Wavelet transform for cardiac image retrieval - p. 117 15. Chapter 8. AI-based diagnosis techniques for cardiac disease analysis and predictions - p. 133 16. Chapter 9. An improved regularization and fitting-based segmentation method for echocardiographic images - p. 157 17. Chapter 10. Identification of heart failure from cine-MRI images using pattern-based features - p. 187 18. Chapter 11. Medical image fusion methods: Review and application in cardiac diagnosis - p. 195 19. Index - p. 217
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Contributors - p. iii 6. Preface - p. xiii 7. Acknowledgment - p. xiv 8. Chapter 1. Cardiac diseases and their diagnosis methods - p. 1 9. Chapter 2. Cardiac multimodal image registration using machine learning techniques - p. 21 10. Chapter 3. Anatomical photo representations for cardiac imaging training - p. 35 11. Chapter 4. Cardiac function review by machine learning approaches - p. 49 12. Chapter 5. Despeckling in echocardiographic images using a hybrid fuzzy filter - p. 77 13. Chapter 6. Impetus to machine learning in cardiac disease diagnosis - p. 99 14. Chapter 7. Wavelet transform for cardiac image retrieval - p. 117 15. Chapter 8. AI-based diagnosis techniques for cardiac disease analysis and predictions - p. 133 16. Chapter 9. An improved regularization and fitting-based segmentation method for echocardiographic images - p. 157 17. Chapter 10. Identification of heart failure from cine-MRI images using pattern-based features - p. 187 18. Chapter 11. Medical image fusion methods: Review and application in cardiac diagnosis - p. 195 19. Index - p. 217
