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Artificial Intelligence and Image Processing in Medical Imaging
- 1st Edition - January 17, 2024
- Editors: Walid A. Zgallai, Dilber Uzun Ozsahin
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 6 2 - 4
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 6 3 - 1
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… Read more
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Request a sales quoteArtificial 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.
- Provides the latest comprehensive coverage of the developments of AI techniques and the principles of medical imaging
- Covers all aspects of medical imaging, from acquisition, the use of hardware and software, image analysis and implementation of AI in problem solving
- Provides examples of medical imaging and how they’re processed, including segmentation, classification, and detection
Researchers and biomedical engineers, Postgraduate Fellows, postgraduate and undergraduate Biomedical Engineering students
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter 1. Introduction to machine learning and artificial intelligence
- Abstract
- 1.1 Comprehensive introduction to machine learning and artificial intelligence
- References
- Chapter 2. Convolution neural network and deep learning
- Abstract
- Abbreviations
- 2.1 Brief history of deep learning
- 2.2 Deep learning
- 2.3 Common terminologies in deep learning
- References
- Chapter 3. Image preprocessing phase with artificial intelligence methods on medical images
- Abstract
- 3.1 Introduction
- 3.2 Medical imaging
- 3.3 Image processing
- 3.4 Histogram equalization
- 3.5 Power-law transformation
- 3.6 Linear transformation
- 3.7 Log transformation
- 3.8 Mean filter
- 3.9 Median filter
- 3.10 Gaussian filter
- 3.11 Image compression
- 3.12 Image enhancement
- 3.13 Image resizing
- 3.14 Image restoration
- 3.15 Image segmentation
- 3.16 Artificial intelligence in medical imaging
- 3.17 Applications of image preprocessing in medical imaging
- 3.18 Simplified: applications of artificial intelligence and image preprocessing in medical imaging
- 3.19 Medical cases
- 3.20 Image processing techniques
- 3.21 Conclusion
- References
- Chapter 4. Artificial intelligence in mammography: advances and challenges
- Abstract
- 4.1 Introduction to mammography as early breast cancer detection tool
- 4.2 Artificial intelligence applications in mammography
- 4.3 Conclusions, challenges, and future directions
- References
- Chapter 5. Segmentation of breast tissue structures in mammographic images
- Abstract
- 5.1 Introduction
- 5.2 Image-based feature analysis for breast cancer classification and diagnosis
- 5.3 Breast region and pectoral muscle segmentation
- 5.4 Characterization and classification of microcalcifications and microcalcification clusters
- 5.5 Segmentation and classification of masses
- 5.6 Conclusion
- References
- Chapter 6. Mammographic breast density segmentation
- Abstract
- 6.1 Introduction
- 6.2 Breast imaging reporting and data system categories of density and tissue patterns
- 6.3 Breast imaging reporting and data system categories of mammogram findings
- 6.4 Breast density segmentation
- 6.5 Breast cancer risk assessment
- 6.6 Conclusion
- References
- Chapter 7. A mathematical resolution in selecting suitable magnetic field-based breast cancer imaging modality: a comparative study on seven diagnostic techniques
- Abstract
- 7.1 Introduction
- 7.2 Materials and methods
- 7.3 Results
- 7.4 Sensitivity analysis
- 7.5 Discussion
- 7.6 Conclusion
- References
- Chapter 8. BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms
- Abstract
- 8.1 Introduction
- 8.2 Materials and methods
- 8.3 Results
- 8.4 Discussion
- 8.5 Conclusion
- References
- Chapter 9. Artificial intelligence in cardiovascular imaging: advances and challenges
- Abstract
- 9.1 Introduction to cardiovascular imaging research
- 9.2 Artificial intelligence in cardiac computed tomography
- 9.3 Artificial intelligence in cardiac magnetic resonance imaging
- 9.4 Artificial intelligence in cardiac ultrasound (echocardiography)
- 9.5 Challenges and future directions
- 9.6 Conclusion
- References
- Chapter 10. Digital conversion and scaling of IgM and IgG antibody test results in COVID-19 diseases
- Abstract
- 10.1 Introduction
- 10.2 Materials and methods
- 10.3 Experiments and results
- 10.4 Discussions
- 10.5 Conclusion
- References
- Chapter 11. Artificial intelligence in dental research and practice
- Abstract
- 11.1 Introduction
- 11.2 Difference between natural and computer intelligence
- 11.3 Deep learning and machine learning
- 11.4 General applications of artificial intelligence in the dental field
- 11.5 Dentistry and artificial intelligence: details of current applications
- 11.6 Conclusion
- 11.7 Difficulties and challenges
- 11.8 Future and scope
- References
- Chapter 12. A-scan generation in spectral domain-optical coherence tomography devices: a survey
- Abstract
- 12.1 Introduction
- 12.2 Spectral domain-optical coherence tomography hardware and its contribution in A-scan generation
- 12.3 Signal processing algorithms required for A-scan extraction
- 12.4 Effects of improper hardware design on spectral domain-optical coherence tomography characteristics
- 12.5 Conclusion
- References
- Chapter 13. Medical image super-resolution
- Abstract
- 13.1 Introduction
- 13.2 A taxonomy of super-resolution algorithms
- 13.3 Applications in medical imaging
- 13.4 Limitations, challenges, and open research directions
- References
- Further reading
- Chapter 14. Class imbalance and its impact on predictive models for binary classification of disease: a comparative analysis
- Abstract
- 14.1 Introduction
- 14.2 Methodology
- 14.3 Result and discussion
- 14.4 Conclusion and limitations
- References
- Index
- No. of pages: 436
- Language: English
- Edition: 1
- Published: January 17, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323954624
- eBook ISBN: 9780323954631
WZ
Walid A. Zgallai
Dr. Walid A. Zgallai earned a PhD in electrical engineering from City University London, United Kingdom and MSc in communications and signal Processing from the University of London, United Kingdom. He has received postgraduate diploma (DIC) from Imperial College of Science, Medicine, and Technology, the University of London, United Kingdom; Postgraduate Certificate in Learning and Teaching from the University of West London, United Kingdom. He serves as the Associate Executive Dean for the Faculty of Engineering Technology and Science at the Higher Colleges of Technology, UAE.
Affiliations and expertise
Division Chair, Faculty of Engineering Technology and Science, Higher Colleges of Technology, Dubai Women’s Campus, Dubai, UAEDO
Dilber Uzun Ozsahin
Dr. Dilber Uzun Ozsahin is an accomplished Associate Professor in the Medical Diagnostic Imaging Department of the College of Health Science at the University of Sharjah. She serves as the director of the Operational Research Centre in Healthcare at the Near East University since 2020 and is establishing a new research group focused on operational research and integrated artificial intelligence in healthcare at the University of Sharjah.
Affiliations and expertise
University of SharjahRead Artificial Intelligence and Image Processing in Medical Imaging on ScienceDirect