Medical Image Analysis
- 1st Edition - September 20, 2023
- Editors: Alejandro Frangi, Jerry Prince, Milan Sonka
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 3 6 5 7 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 3 6 5 8 - 4
Medical imaging is increasingly at the base of many breakthroughs in biomedical sciences, becoming a fundamental enabling technology of biomedical scientific progress. Medical Im… Read more
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Request a sales quoteMedical 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.
- An authoritative presentation of key concepts and methods from experts in the field
- Sections clearly explaining key methodological principles within relevant medical applications
- Self-contained chapters enable the text to be used on courses with differing structures
- A representative selection of modern topics and techniques in medical image computing
- Focus on medical image computing as an enabling technology to tackle unmet clinical needs
- Presentation of traditional and machine learning approaches to medical image computing
Graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find methods and software tools for analyzing medical images
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Section editors
- Editors
- Contributors
- Preface
- Nomenclature
- Acknowledgments
- Part I: Introductory topics
- Chapter 1: Medical imaging modalities
- Abstract
- 1.1. Introduction
- 1.2. Image quality
- 1.3. Modalities and contrast mechanisms
- 1.4. Clinical scenarios
- 1.5. Exercises
- References
- Chapter 2: Mathematical preliminaries
- Abstract
- 2.1. Introduction
- 2.2. Imaging: definitions, quality and similarity measures
- 2.3. Vector and matrix theory results
- 2.4. Linear processing and transformed domains
- 2.5. Calculus
- 2.6. Notions on shapes
- 2.7. Exercises
- References
- Chapter 3: Regression and classification
- Abstract
- Nomenclature
- 3.1. Introduction
- 3.2. Multidimensional linear regression
- 3.3. Treating non-linear problems with linear models
- 3.4. Exercises
- References
- Chapter 4: Estimation and inference
- Abstract
- 4.1. Introduction: what is estimation?
- 4.2. Sampling distributions
- 4.3. Estimation. Data-based methods
- 4.4. A working example
- 4.5. Estimation. Bayesian methods
- 4.6. Monte Carlo methods
- 4.7. Exercises
- References
- Part II: Image representation and processing
- Chapter 5: Image representation and 2D signal processing
- Abstract
- 5.1. Image representation
- 5.2. Images as 2D signals
- 5.3. Frequency representation of 2D signals
- 5.4. Image sampling
- 5.5. Image interpolation
- 5.6. Image quantization
- 5.7. Further reading
- 5.8. Exercises
- References
- Chapter 6: Image filtering: enhancement and restoration
- Abstract
- 6.1. Medical imaging filtering
- 6.2. Point-to-point operations
- 6.3. Spatial operations
- 6.4. Operations in the transform domain
- 6.5. Model-based filtering: image restoration
- 6.6. Further reading
- 6.7. Exercises
- References
- Chapter 7: Multiscale and multiresolution analysis
- Abstract
- 7.1. Introduction
- 7.2. The image pyramid
- 7.3. The Gaussian scale-space
- 7.4. Properties of the Gaussian scale-space
- 7.5. Scale selection
- 7.6. The scale-space histogram
- 7.7. Exercises
- References
- Part III: Medical image segmentation
- Chapter 8: Statistical shape models
- Abstract
- 8.1. Introduction
- 8.2. Representing structures with points
- 8.3. Comparing shapes
- 8.4. Aligning two shapes
- 8.5. Aligning a set of shapes
- 8.6. Building shape models
- 8.7. Statistical models of texture
- 8.8. Combined models of appearance (shape and texture)
- 8.9. Image search
- 8.10. Exhaustive search
- 8.11. Alternating approaches
- 8.12. Constrained local models
- 8.13. 3D models
- 8.14. Recapitulation
- 8.15. Exercises
- References
- Chapter 9: Segmentation by deformable models
- Abstract
- 9.1. Introduction
- 9.2. Boundary evolution
- 9.3. Forces and speed functions
- 9.4. Numerical implementation
- 9.5. Other considerations
- 9.6. Recapitulation
- 9.7. Exercises
- References
- Chapter 10: Graph cut-based segmentation
- Abstract
- 10.1. Introduction
- 10.2. Graph theory
- 10.3. Modeling image segmentation using Markov random fields
- 10.4. Energy function, image term, and regularization term
- 10.5. Graph optimization and necessary conditions
- 10.6. Interactive segmentation
- 10.7. More than two labels
- 10.8. Recapitulation
- 10.9. Exercises
- References
- Part IV: Medical image registration
- Chapter 11: Points and surface registration
- Abstract
- 11.1. Introduction
- 11.2. Points registration
- 11.3. Surface registration
- 11.4. Summary
- 11.5. Exercises
- References
- Chapter 12: Graph matching and registration
- Abstract
- 12.1. Introduction
- 12.2. Graph-based image registration
- 12.3. Exercises
- References
- Chapter 13: Parametric volumetric registration
- Abstract
- 13.1. Introduction to volumetric registration
- 13.2. Mathematical concepts
- 13.3. Parametric volumetric registration
- 13.4. Exercises
- References
- Chapter 14: Non-parametric volumetric registration
- Abstract
- 14.1. Introduction
- 14.2. Mathematical concepts
- 14.3. Optical flow and related non-parametric methods
- 14.4. Large deformation diffeomorphic metric mapping
- 14.5. Exercises
- References
- Chapter 15: Image mosaicking
- Abstract
- 15.1. Introduction
- 15.2. Motion models
- 15.3. Matching
- 15.4. Clinical applications
- 15.5. Recapitulation
- 15.6. Exercises
- References
- Part V: Machine learning in medical image analysis
- Chapter 16: Deep learning fundamentals
- Abstract
- 16.1. Introduction
- 16.2. Learning as optimization
- 16.3. Inductive bias, invariance, and equivariance
- 16.4. Recapitulation
- 16.5. Further reading
- 16.6. Exercises
- References
- Chapter 17: Deep learning for vision and representation learning
- Abstract
- 17.1. Introduction
- 17.2. Convolutional neural networks
- 17.3. Deep representation learning
- 17.4. Recapitulation
- 17.5. Further reading
- 17.6. Exercises
- References
- Chapter 18: Deep learning medical image segmentation
- Abstract
- 18.1. Introduction
- 18.2. Convolution-based deep learning segmentation
- 18.3. Transformer-based deep learning segmentation
- 18.4. Hybrid deep learning segmentation
- 18.5. Training efficiency
- 18.6. Explainability
- 18.7. Case study
- 18.8. Recapitulation
- 18.9. Further reading
- 18.10. Exercises
- References
- Chapter 19: Machine learning in image registration
- Abstract
- 19.1. Introduction
- 19.2. Image registration with deep learning
- 19.3. Deep neural network architecture
- 19.4. Supervised image registration
- 19.5. Unsupervised image registration
- 19.6. Recapitulation
- 19.7. Exercises
- References
- Part VI: Advanced topics in medical image analysis
- Chapter 20: Motion and deformation recovery and analysis
- Abstract
- 20.1. Introduction
- 20.2. The unmet clinical need
- 20.3. Image-centric flow fields: Eulerian analysis
- 20.4. Object-centric, locally derived flow fields: Lagrangian analysis
- 20.5. Multiframe analysis: Kalman filters, particle tracking
- 20.6. Advanced strategies: model-based analysis and data-driven deep learning
- 20.7. Evaluation
- 20.8. Recapitulation
- 20.9. Exercises
- References
- Chapter 21: Imaging Genetics
- Abstract
- 21.1. Introduction
- 21.2. Genome-wide association studies
- 21.3. Multivariate approaches to imaging genetics
- 21.4. Exercises
- References
- Part VII: Large-scale databases
- Chapter 22: Detection and quantitative enumeration of objects from large images
- Abstract
- 22.1. Introduction
- 22.2. Classical image analysis methods
- 22.3. Learning from data
- 22.4. Detection and counting of mitotic cells using Bayesian modeling and classical image processing
- 22.5. Detection and counting of nuclei using deep learning
- 22.6. Recapitulation
- 22.7. Exercises
- References
- Chapter 23: Image retrieval in big image data
- Abstract
- 23.1. Introduction
- 23.2. Global image descriptors for image retrieval
- 23.3. Deep learning-based image retrieval
- 23.4. Efficient indexing strategies
- 23.5. Exercises
- References
- Part VIII: Evaluation in medical image analysis
- Chapter 24: Assessment of image computing methods
- Abstract
- 24.1. The fundamental methodological concept
- 24.2. Introduction
- 24.3. Evaluation for classification tasks
- 24.4. Learning and validation
- 24.5. Evaluation for segmentation tasks
- 24.6. Evaluation of registration tasks
- 24.7. Intra-rater and inter-rater comparisons
- 24.8. Recapitulation
- 24.9. Exercises
- References
- Index
- No. of pages: 698
- Language: English
- Edition: 1
- Published: September 20, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780128136577
- eBook ISBN: 9780128136584
AF
Alejandro Frangi
Alejandro F. Frangi is the Bicentennial Turing Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at The University of Manchester, Manchester, UK, with joint appointments at the Schools of Engineering (Department of Computer Science), Faculty of Science and Engineering, and the School of Health Sciences (Division of Informatics, Imaging and Data Science), Faculty of Biology, Medicine and Health.
He is a Turing Fellow of the Alan Turing
Institute. He holds an Honorary Chair at KU Leuven in the Departments of Electrical Engineering (ESAT) and Cardiovascular Science. He is IEEE Fellow (2014), EAMBES Fellow (2015), SPIE Fellow (2020), MICCAI Fellow (2021), and Royal Academy of Engineering Fellow (2023). The IEEE Engineering in Medicine and Biology Society awarded him the Early Career Award (2006) and Technical Achievement Award (2021). Professor Frangi’s primary research interests are in medical image analysis and modeling, emphasising machine learning (phenomenological models) and computational physiology (mechanistic models). He is an expert in statistical
shape modeling, computational anatomy, and image-based computational physiology, delivering novel insights and impact across various imaging modalities and diseases,
particularly on cardiovascular MRI, cerebrovascular MRI/CT/3DRA, and musculoskeletal CT/DXA. He is a co-founder of adsilico Ltd., and his work led to products
commercialized by GalgoMedical SA. He has published over 285 peer-reviewed papers in scientific journals with over 34,000 citations and has an h-index of 75.
Affiliations and expertise
Department of Computer Science, School of Engineering, Faculty of Science and Engineering, The University of Manchester, Manchester, United KingdomJP
Jerry Prince
Jerry L. Prince is the William B. Kouwenhoven Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University. He is Director of the Image Analysis and Communications Laboratory (IACL). He also holds joint appointments in the Departments of Radiology and Radiological Science, Biomedical Engineering, Computer Scienceand Applied Mathematics and Statistics at Johns Hopkins University.
He received a 1993 National Science Foundation Presidential Faculty Fellows Award, was Maryland’s 1997 Outstanding Young Engineer, and was awarded the MICCAI Society Enduring Impact Award in 2012. He is an IEEE Fellow, MICCAI Fellow, and AIMBE Fellow. Previously he was an Associate Editor of IEEE Transactions on Image Processing and an Associate Editor of IEEE Transactions on Medical Imaging. He is currently a member of the Editorial Boards of Medical Image Analysis and the Proceedings of the IEEE. He is cofounder of Sonavex, Inc., a biotech company located in Baltimore, Maryland, USA. His current research interests include image processing, computer vision, and machine learning
with primary application to medical imaging, he has published over 500 articles on these subjects.
Affiliations and expertise
Professor of Electrical and Computer Engineering, Johns Hopkins University, USAMS
Milan Sonka
Milan Sonka is Professor of Electrical & Computer Engineering, Biomedical Engineering, Ophthalmology & Visual Sciences, and Radiation Oncology, and Lowell C. Battershell Chair in Biomedical Imaging, all at the University of Iowa. He served as Chair of the Department of Electrical and Computer Engineering (2008–2014) and as Associate Dean for Research and Graduate Studies (2014–2019). He is a Fellow of IEEE, Fellow of the American Institute of Medical and Biological Engineers (AIMBE), Fellow of the Medical Image Computing and Computer-Aided Intervention Society (MICCAI), and Fellow of the National Academy of Inventors.
He is the Founding Codirector of an interdisciplinary Iowa Institute for Biomedical Imaging (2007–) and Founding Director of the Iowa Initiative for Artificial Intelligence (2019–). He is the author of four editions of an image processing textbook, Image Processing, Analysis, and Machine Vision (1993, 1998, 2008, 2014), editor of one of three volumes of the SPIE Handbook of Medical Imaging (2000), past Editor-in-Chief of “IEEE Transactions on Medical Imaging” (2009–2014), and past editorial board member of the “Medical Image Analysis” journal. His >700 publications were cited more than 42,000 times, and he has an h-index of 80. He cofounded Medical Imaging Applications LLC and VIDA Diagnostics Inc.
Affiliations and expertise
Professor of Electrical and Computer Engineering, The University of Iowa, USARead Medical Image Analysis on ScienceDirect