The aim of this book is to explain the process of biomedical imaging, from image acquisition to automated diagnosis. This process consists of three thematic areas. The first is dedicated to the acquisition process and the underlying properties of images from a physics-oriented perspective. The second part addresses the dominant state-of-the-art methodologies behind content extraction and interpretation of medical images. The third section presents an application-based example, which develops solutions to address the particular needs of various diagnoses.
This complete volume is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors. This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses.
Nikos Paragios (D.Sc. (2005), PhD (2000), M.Sc. (1996), B.Sc. (1994)) is professor of Applied Mathematics, director of the Center for Visual Computing of Ecole Centrale de Paris, France and scientific leader of GALEN project-team of Ecole Centrale de Paris/Inria Saclay, Ile-de-France. Professor Paragios is an IEEE Fellow (2012), has co-edited four books, published more than two hundred fifty papers in the most prestigious journals and conferences of medical imaging and computer vision, and holds twenty one international patents (http://cvn.ecp.fr/personnel/nikos/). Professor Paragios is the Editor in Chief of the Computer Vision and Image Understanding Journal (CVIU), member of the editorial board of the the Medical Image Analysis Journal(MedIA) and the SIAM Journal in Imaging Sciences (SIIMS) , while has served as an associate/area editor/member of the editorial board for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), the International Journal of Computer Vision (IJCV), the Journal of Mathematical Imaging and Vision (JMIV), the Imaging and Vision Computing Journal (IVC), the Machine Vision and Applications (MVA) Journal. Professor Paragios serves regularly at the conference boards of the most prestigious events of his field (ICCV, CVPR, ECCV, MICCAI) while being member of the scientific council of SAFRAN conglomerate.
Nicholas Ayache is a Research Director at INRIA, France, and the scientific leader of the ASCLEPIOS project-team in Sophia Antipolis. His current research interests include the analysis and simulation of biomedical images with advanced geometrical, statistical,biophysical and functional models, and the application of these tools to medicine to improve the diagnosis and therapy of diseases. His publications, which have received over 25,000 citations (according to scholar), may be found at: http://www-sop.inria.fr/members/Nicholas.Ayache. Dr. Ayache received in 2008 the "Microsoft Award for Science in Europe" awarded jointly by the Royal Society and the French Academy of Sciences. In 2013 he received in Nagoya (Japan) the 2013 Miccai Enduring Impact Award. For the academic year 2013-2014, Dr. Ayache was elected a professor at the Collège de France, on the Chair Informatics and Digital Sciences. Dr. Ayache has been a scientific consultant for several companies and international research institutes, and has been a co-founder of several start-up companies in image processing, computer vision, and biomedical imaging. Dr. Ayache has been a founding Editor of the Medical Image Analysis Journal (Elsevier Science), an associate Editor of IEEE Trans. on Medical Imaging, and the general Chair of the MICCAI 2012 conference in Nice.
James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering and a Professor of Diagnostic Radiology and Electrical Engineering at Yale University. Professor Duncan received his B.S.E.E. with honors from Lafayette College (1973), his M.S. (1975) from UCLA and Ph.D. (1982) in Electrical Engineering from the University of Southern California. Professor Duncan has been at Yale University since 1983, and the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University since 2007. He has served as the Acting Chair and is currently Director of Undergraduate Studies for Biomedical Engineering. His research efforts have been in the areas of computer vision, image processing, and medical imaging, with an emphasis on biomedical image analysis. These efforts have included the segmentation of deformable structure from 3D image data, the tracking of non-rigid motion/deformation from spatiotemporal images, and the development of strategies for image-guided intervention/surgery. He has published over 220 peer-reviewed articles in these areas and has been the principal investigator on a number of peer-reviewed grants from both the National Institutes of Health and the National Science Foundation over the past 28 years. Professor Duncan is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE), of the American Institute for Medical and Biological Engineering (AIMBE) and of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He currently serves as co-Editor-in-Chief of Medical Image Analysis and as an Associate Editor of IEEE Transactions on Medical Imaging. In 2012, he was elected to the Council of Distinguished Investigators, Academy of Radiology Research. In 2014, he was elected to the Connecticut Academy of Science and Engineering.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.