Artificial Intelligence and Image Processing in Medical Imaging

Walid A. Zgallai,Dilber Uzun Ozsahin

• Verlag: Academic Press
• Jahr: 2024
• ISBN:
  9780323954631
• Thema: Ciencias biológicas
• Thema: Biotecnología

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.

• 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

Inhaltsverzeichnis

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Chapter 1. Introduction to machine learning and artificial intelligence
• Chapter 2. Convolution neural network and deep learning
• Chapter 3. Image preprocessing phase with artificial intelligence methods on medical images
• Chapter 4. Artificial intelligence in mammography: advances and challenges
• Chapter 5. Segmentation of breast tissue structures in mammographic images
• Chapter 6. Mammographic breast density segmentation
• Chapter 7. A mathematical resolution in selecting suitable magnetic field-based breast cancer imaging modality: a comparative study on seven diagnostic techniques
• Chapter 8. BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms
• Chapter 9. Artificial intelligence in cardiovascular imaging: advances and challenges
• Chapter 10. Digital conversion and scaling of IgM and IgG antibody test results in COVID-19 diseases
• Chapter 11. Artificial intelligence in dental research and practice
• Chapter 12. A-scan generation in spectral domain-optical coherence tomography devices: a survey
• Chapter 13. Medical image super-resolution
• Chapter 14. Class imbalance and its impact on predictive models for binary classification of disease: a comparative analysis
• Index