Medical Image Analysis

by Alejandro Frangi (Editor), Jerry Prince (Editor), Milan Sonka (Editor)

Overview

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - 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

Product Details

ISBN-13:	9780128136584
Publisher:	Elsevier Science & Technology Books
Publication date:	09/20/2023
Series:	The MICCAI Society book Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	584
File size:	75 MB
Note:	This product may take a few minutes to download.

About the Author

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.
Jerry L. Prince has research interests in image processing and computer vision with primary application to medical imaging. He has studied and developed methods for imaging motion in the heart, tongue, and brain using magnetic resonance imaging. He has applied both statistical estimation and computer vision methods to the analysis of brain structure with applications in normal aging, Alzheimer’s disease, and multiple sclerosis. He is also co founder of Diagnosoft, Inc., a medical imaging software company. He received the BS degree from the University of Connecticut in 1979 and the S.M., E.E., and PhD degrees in 1982, 1986, and 1988, respectively, from the Massachusetts Institute of Technology, all in electrical engineering and computer science.
Dr. Milan Sonka, Ph.D., is the Co-Founder of VIDA Diagnostics, Inc. Dr. Sonka has been a Professor of Electrical and Computer Engineering at University of Iowa since 2000, Ophthalmology and Visual Sciences since 2006, Applied Mathematical and Computational Sciences since 2001 and Radiation Oncology since 2006. He also serves as Co-director of Iowa Institute for Biomedical Imaging at the University of Iowa. He served as an Assistant Professor at Department of Control Engineering, Czech Technical University of Prague from 1984 to 1990. He served as Visiting Assistant Professor at Department of Electrical and Computer Engineering, The University of Iowa from 1990 to 1993. He served as Visiting Associate Professor from 1993 to 1994 and Associate Professor from 1994 to 2000. Dr. Sonka is a well-known scholar in the area of quantitative medical image analysis with a record of successful commercialization of his cardiovascular and pulmonary image analysis methods and approaches.

Table of Contents

PART I Introductory topics1. Medical imaging modalities2. Mathematical preliminaries3. Regression and classification4. Estimation and inferencePART II Image representation and processing5. Image representation and 2D signal processing6. Image filtering: enhancement and restoration7. Multiscale and multiresolution analysisPART III Medical image segmentation8. Statistical shape models9. Segmentation by deformable models10. Graph cut-based segmentationPART IV Medical image registration11. Points and surface registration12. Graph matching and registration13. Parametric volumetric registration14. Non-parametric volumetric registration15. Image mosaickingPART V Machine learning in medical image analysis16. Deep learning fundamentals17. Deep learning for vision and representation learning18. Deep learning medical image segmentation19. Machine learning in image registrationPART VI Advanced topics in medical image analysis20. Motion and deformation recovery and analysis21. Imaging GeneticsPART VII Large-scale databases22. Detection and quantitative enumeration of objects from large images23. Image retrieval in big image dataPART VIII Evaluation in medical image analysis24. Assessment of image computing methods

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From the Publisher

Gives graduates and researchers an understanding of medical image analysis methods and their applications