Artificial Intelligence and Image Processing in Medical Imaging deals with the applications of processing medical images with a view of improving the quality of the data in order to facilitate better decision- making. The book covers the basics of medical imaging and the fundamentals of image processing. It explains spatial and frequency domain applications of image processing, introduces image compression techniques and their applications, and covers image segmentation techniques and their applications. The book includes object detection and classification applications and provides an overall background to statistical analysis in biomedical systems.
The role of Machine Learning, including Neural Networks, Deep Learning, and the implications of the expansion of artificial intelligence is also covered. With contributions from prominent researchers worldwide, this book provides up-to-date and comprehensive coverage of AI applications in image processing where readers will find the latest information with clear examples and illustrations.
1. Chapter 1 : Introduction to machine learning and artificial intelligence - p. Page ix 2. Chapter 2 : Convolution neural network and deep learning - p. Page 16 3. Chapter 3 : Image preprocessing phase with artificial intelligence methods on medical images - p. Page 42 4. Chapter 4 : Artificial intelligence in mammography: advances and challenges - p. Page 75 5. Chapter 5 : Segmentation of breast tissue structures in mammographic images - p. Page 107 6. Chapter 6 : Mammographic breast density segmentation - p. Page 136 7. Chapter 7 : A mathematical resolution in selecting suitable magnetic field-based breast cancer imaging modality: a comparative study on seven diagnostic techniques - p. Page 164 8. Chapter 8 : BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms - p. Page 191 9. Chapter 9 : Artificial intelligence in cardiovascular imaging: advances and challenges - p. Page 212 10. Chapter 10 : Digital conversion and scaling of IgM and IgG antibody test results in COVID-19 diseases - p. Page 242 11. Chapter 11 : Artificial intelligence in dental research and practice - p. Page 265 12. Chapter 12 : A-scan generation in spectral domain-optical coherence tomography devices: a survey - p. Page 288 13. Chapter 13 : Medical image super-resolution - p. Page 316 14. Chapter 14 : Class imbalance and its impact on predictive models for binary classification of disease: a comparative analysis - p. Page 375 15. Index - p. Page 407
Artificial Intelligence and Image Processing in Medical Imaging deals with the applications of processing medical images with a view of improving the quality of the data in order to facilitate better decision- making. The book covers the basics of medical imaging and the fundamentals of image processing. It explains spatial and frequency domain applications of image processing, introduces image compression techniques and their applications, and covers image segmentation techniques and their applications. The book includes object detection and classification applications and provides an overall background to statistical analysis in biomedical systems.
The role of Machine Learning, including Neural Networks, Deep Learning, and the implications of the expansion of artificial intelligence is also covered. With contributions from prominent researchers worldwide, this book provides up-to-date and comprehensive coverage of AI applications in image processing where readers will find the latest information with clear examples and illustrations.
1. Chapter 1 : Introduction to machine learning and artificial intelligence - p. Page ix 2. Chapter 2 : Convolution neural network and deep learning - p. Page 16 3. Chapter 3 : Image preprocessing phase with artificial intelligence methods on medical images - p. Page 42 4. Chapter 4 : Artificial intelligence in mammography: advances and challenges - p. Page 75 5. Chapter 5 : Segmentation of breast tissue structures in mammographic images - p. Page 107 6. Chapter 6 : Mammographic breast density segmentation - p. Page 136 7. Chapter 7 : A mathematical resolution in selecting suitable magnetic field-based breast cancer imaging modality: a comparative study on seven diagnostic techniques - p. Page 164 8. Chapter 8 : BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms - p. Page 191 9. Chapter 9 : Artificial intelligence in cardiovascular imaging: advances and challenges - p. Page 212 10. Chapter 10 : Digital conversion and scaling of IgM and IgG antibody test results in COVID-19 diseases - p. Page 242 11. Chapter 11 : Artificial intelligence in dental research and practice - p. Page 265 12. Chapter 12 : A-scan generation in spectral domain-optical coherence tomography devices: a survey - p. Page 288 13. Chapter 13 : Medical image super-resolution - p. Page 316 14. Chapter 14 : Class imbalance and its impact on predictive models for binary classification of disease: a comparative analysis - p. Page 375 15. Index - p. Page 407
