Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
Fichtinger, Gabor: - Professor Gabor Fichtinger is a Canada Research Chair in Computer-Integrated Surgery, at the School of Computing, Queen's University, Canada. His research and teaching interests are Computer-Assisted Interventions, involving medical imaging, medical image analysis, visualization, surgical planning and navigation, robotics, biosensors, and integrating these component technologies into workable clinical systems. He further specializes in minimally invasive percutaneous (through the skin) interventions performed under image guidance, with primary application in the detection and treatment of cancer. He is an associate editor of IEEE Transactions on Biomedical Engineering, a member of the editorial board of Medical Image Analysis, and a deputy editor for the International Journal of Computer-Assisted Radiology and Surgery. He has served on the program and organizing committees of leading international conferences, including SPIE Medical Imaging and IPCAI; he was general co-chair for MICCAI 2011, and program co-chair for MICCAI 2008 and 2018. Professor Fichtinger is a Fellow of IEEE and a Fellow of MICCAI.
Rueckert, Daniel: - Professor Daniel Rueckert is Head of the Department of Computing at Imperial College London. He joined the Department of Computing as a lecturer in 1999 and became senior lecturer in 2003. Since 2005 he is Professor of Visual Information Processing. He has founded and leads the Biomedical Image Analysis group. His research interests include: Development of algorithms for image acquisition, image analysis and image interpretation, in particular in the areas of reconstruction, registration, tracking, segmentation and modelling; and novel machine learning approaches for the extraction of clinically useful information from medical images with application to computer-aided detection and diagnosis, computer-aided treatment planning, computer-guided interventions and therapy. He is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing, MICCAI/Elsevier Book Series, and a referee for a number of international medical imaging journals and conferences. He has served as a member of organizing and program committees at numerous conferences, e.g. general co-chair of MMBIA 2006 and FIMH 2013 as well as program co-chair of MICCAI 2009, ISBI 2012 and WBIR 2012. He was elected as a Fellow of MICCAI in 2014, Fellow of the Royal Academy of Engineering in 2015 and, most recently, a Fellow of the Academy of Medical Sciences in 2019.
Zhou, S. Kevin: - S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer..
