Medical imaging is increasingly at the base of many breakthroughs in biomedical sciences, becoming a fundamental enabling technology of biomedical scientific progress. Medical Image Analysis presents practical knowledge on medical image computing and analysis and is written by top educators and experts in the field. This text is a modern, practical, broad, and self-contained reference that conveys a mix of essential methodological concepts within different medical domains, reflecting the nature of the discipline today, making it suitable as a course text and a self-learning resource.
Medical imaging is increasingly at the base of many breakthroughs in biomedical sciences, becoming a fundamental enabling technology of biomedical scientific progress. Medical Image Analysis presents practical knowledge on medical image computing and analysis and is written by top educators and experts in the field. This text is a modern, practical, broad, and self-contained reference that conveys a mix of essential methodological concepts within different medical domains, reflecting the nature of the discipline today, making it suitable as a course text and a self-learning resource.
Alejandro (Alex) Frangi is Professor of Biomedical Image Computing at the University of Sheffield (USFD) and affiliated to the Electronic & Electrical Engineering Department. He is also Director of the Center for Computational Imaging and Simulation Technologies in Biomedicine and member of INSIGNEO Institute for in silico Medicine. Prof Frangi is Fellow of IEEE. His main research interests are in medical image computing, medical imaging and image-based computational physiology. Prof. Frangi has edited a book, published 5 editorial articles and over 90 journal papers in key international journals of his research field, as well as more than 120 book chapters and international conference papers. He has twice been Guest Editor of special issues of IEEE Trans on Medical Imaging, one on IEEE Trans Biomed Eng, and one of Medical Image Analysis journal.
Inhaltsangabe
PART I Introductory topics 1. Medical imaging modalities 2. Mathematical preliminaries 3. Regression and classification 4. Estimation and inference PART II Image representation and processing 5. Image representation and 2D signal processing 6. Image filtering: enhancement and restoration 7. Multiscale and multiresolution analysis PART III Medical image segmentation 8. Statistical shape models 9. Segmentation by deformable models 10. Graph cut-based segmentation PART IV Medical image registration 11. Points and surface registration 12. Graph matching and registration 13. Parametric volumetric registration 14. Non-parametric volumetric registration 15. Image mosaicking PART V Machine learning in medical image analysis 16. Deep learning fundamentals 17. Deep learning for vision and representation learning 18. Deep learning medical image segmentation 19. Machine learning in image registration PART VI Advanced topics in medical image analysis 20. Motion and deformation recovery and analysis 21. Imaging Genetics PART VII Large-scale databases 22. Detection and quantitative enumeration of objects from large images 23. Image retrieval in big image data PART VIII Evaluation in medical image analysis 24. Assessment of image computing methods
PART I Introductory topics 1. Medical imaging modalities 2. Mathematical preliminaries 3. Regression and classification 4. Estimation and inference PART II Image representation and processing 5. Image representation and 2D signal processing 6. Image filtering: enhancement and restoration 7. Multiscale and multiresolution analysis PART III Medical image segmentation 8. Statistical shape models 9. Segmentation by deformable models 10. Graph cut-based segmentation PART IV Medical image registration 11. Points and surface registration 12. Graph matching and registration 13. Parametric volumetric registration 14. Non-parametric volumetric registration 15. Image mosaicking PART V Machine learning in medical image analysis 16. Deep learning fundamentals 17. Deep learning for vision and representation learning 18. Deep learning medical image segmentation 19. Machine learning in image registration PART VI Advanced topics in medical image analysis 20. Motion and deformation recovery and analysis 21. Imaging Genetics PART VII Large-scale databases 22. Detection and quantitative enumeration of objects from large images 23. Image retrieval in big image data PART VIII Evaluation in medical image analysis 24. Assessment of image computing methods
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