Stochastic Modeling For Medical Image Analysis
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| Author | : Ayman El-Baz |
| Publisher | : CRC Press |
| Total Pages | : 299 |
| Release | : 2015-11-18 |
| Genre | : Medical |
| ISBN | : 1466599081 |
Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis.Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obt
| Author | : Xavier Descombes |
| Publisher | : Wiley-ISTE |
| Total Pages | : 0 |
| Release | : 2011-12-12 |
| Genre | : Technology & Engineering |
| ISBN | : 9781848212404 |
This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.
| Author | : Chee Sun Won |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 176 |
| Release | : 2013-11-27 |
| Genre | : Computers |
| ISBN | : 1441988572 |
Stochastic Image Processing provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing. Although promoted as a promising approach for over thirty years, it has only been in the past few years that the theory and algorithms have developed to the point of providing useful solutions to old and new problems in image processing. Markov random fields are a multidimensional extension of Markov chains, but the generalization is complicated by the lack of a natural ordering of pixels in multidimensional spaces. Hidden Markov fields are a natural generalization of the hidden Markov models that have proved essential to the development of modern speech recognition, but again the multidimensional nature of the signals makes them inherently more complicated to handle. This added complexity contributed to the long time required for the development of successful methods and applications. This book collects together a variety of successful approaches to a complete and useful characterization of multidimensional Markov and hidden Markov models along with applications to image analysis. The book provides a survey and comparative development of an exciting and rapidly evolving field of multidimensional Markov and hidden Markov random fields with extensive references to the literature.
| Author | : Piero Barone |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 266 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461229200 |
This volume comprises a collection of papers by world- renowned experts on image analysis. The papers range from survey articles to research papers, and from theoretical topics such as simulated annealing through to applied image reconstruction. It covers applications as diverse as biomedicine, astronomy, and geophysics. As a result, any researcher working on image analysis will find this book provides an up-to-date overview of the field and in addition, the extensive bibliographies will make this a useful reference.
| Author | : Tony F. Chan |
| Publisher | : SIAM |
| Total Pages | : 414 |
| Release | : 2005-09-01 |
| Genre | : Computers |
| ISBN | : 089871589X |
This book develops the mathematical foundation of modern image processing and low-level computer vision, bridging contemporary mathematics with state-of-the-art methodologies in modern image processing, whilst organizing contemporary literature into a coherent and logical structure. The authors have integrated the diversity of modern image processing approaches by revealing the few common threads that connect them to Fourier and spectral analysis, the machinery that image processing has been traditionally built on. The text is systematic and well organized: the geometric, functional, and atomic structures of images are investigated, before moving to a rigorous development and analysis of several image processors. The book is comprehensive and integrative, covering the four most powerful classes of mathematical tools in contemporary image analysis and processing while exploring their intrinsic connections and integration. The material is balanced in theory and computation, following a solid theoretical analysis of model building and performance with computational implementation and numerical examples.
| Author | : Jiri Jan |
| Publisher | : CRC Press |
| Total Pages | : 574 |
| Release | : 2019-08-30 |
| Genre | : Medical |
| ISBN | : 135138791X |
Differently oriented specialists and students involved in image processing and analysis need to have a firm grasp of concepts and methods used in this now widely utilized area. This book aims at being a single-source reference providing such foundations in the form of theoretical yet clear and easy to follow explanations of underlying generic concepts. Medical Image Processing, Reconstruction and Analysis – Concepts and Methods explains the general principles and methods of image processing and analysis, focusing namely on applications used in medical imaging. The content of this book is divided into three parts: Part I – Images as Multidimensional Signals provides the introduction to basic image processing theory, explaining it for both analogue and digital image representations. Part II – Imaging Systems as Data Sources offers a non-traditional view on imaging modalities, explaining their principles influencing properties of the obtained images that are to be subsequently processed by methods described in this book. Newly, principles of novel modalities, as spectral CT, functional MRI, ultrafast planar-wave ultrasonography and optical coherence tomography are included. Part III – Image Processing and Analysis focuses on tomographic image reconstruction, image fusion and methods of image enhancement and restoration; further it explains concepts of low-level image analysis as texture analysis, image segmentation and morphological transforms. A new chapter deals with selected areas of higher-level analysis, as principal and independent component analysis and particularly the novel analytic approach based on deep learning. Briefly, also the medical image-processing environment is treated, including processes for image archiving and communication. Features Presents a theoretically exact yet understandable explanation of image processing and analysis concepts and methods Offers practical interpretations of all theoretical conclusions, as derived in the consistent explanation Provides a concise treatment of a wide variety of medical imaging modalities including novel ones, with respect to properties of provided image data
| Author | : Ayman El-Baz |
| Publisher | : CRC Press |
| Total Pages | : 463 |
| Release | : 2019-06-26 |
| Genre | : Medical |
| ISBN | : 1351373021 |
Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based on well-established mathematical theories. Level set method is a great tool for modeling time varying medical images and enhancement of numerical computations.
| Author | : Howard M. Taylor |
| Publisher | : Academic Press |
| Total Pages | : 410 |
| Release | : 2014-05-10 |
| Genre | : Mathematics |
| ISBN | : 1483269272 |
An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
| Author | : Tianhu Lei |
| Publisher | : CRC Press |
| Total Pages | : 426 |
| Release | : 2011-12-19 |
| Genre | : Mathematics |
| ISBN | : 1420088432 |
More work is being done in the statistical aspects of medical imaging, and this book fills the gap to provide a unified framework of study by presenting a complete look at medical imaging and statistics - from the statistical aspects of imaging technology to the statistical analysis of images. It provides technicians and students with the statistical principles that underlay medical imaging, as required reference material for researchers involved in the design of new technology. Illustrations are included throughout as are many real examples, and algorithms. The text also includes exercises developed out of the author's many years experience with studying the statistics of medical imaging.
| Author | : Maxime Descoteaux |
| Publisher | : Springer |
| Total Pages | : 803 |
| Release | : 2017-09-03 |
| Genre | : Computers |
| ISBN | : 331966185X |
The three-volume set LNCS 10433, 10434, and 10435 constitutes the refereed proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, held inQuebec City, Canada, in September 2017. The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.
