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Sinopsis
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.
- 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
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Acerca de los autores
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.
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.
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.
De la contraportada
Medical imaging is increasingly at the base of many breakthroughs in biomedical sciences, becoming a fundamental enabling technology of biomedical scientific progress. Medical Imaging 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.
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- EditorialAcademic Press
- Año de publicación2023
- ISBN 10 012813657X
- ISBN 13 9780128136577
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas698
- EditorFrangi, Prince, Sonka
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