Stochastic Modeling for Medical Image Analysis PDF Download

Author: Ayman El-Baz
Publisher: CRC Press
ISBN: 1466599081
Category : Medical
Languages : en
Pages : 299

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Book Description
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: John Wiley & Sons
ISBN: 1118601130
Category : Technology & Engineering
Languages : en
Pages : 215

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Book Description
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: Barry L. Nelson
Publisher: Courier Corporation
ISBN: 0486139948
Category : Mathematics
Languages : en
Pages : 338

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Book Description
Coherent introduction to techniques also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Includes formulation of models, analysis, and interpretation of results. 1995 edition.

Author: Ya-Qin Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description

Author: Piero Barone
Publisher: Springer Science & Business Media
ISBN: 1461229200
Category : Mathematics
Languages : en
Pages : 266

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Book Description
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: Chee Sun Won
Publisher: Springer Science & Business Media
ISBN: 1441988572
Category : Computers
Languages : en
Pages : 176

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Book Description
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: Howard M. Taylor
Publisher: Academic Press
ISBN: 1483269272
Category : Mathematics
Languages : en
Pages : 410

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Book Description
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: Ayman Sabry El-Baz
Publisher:
ISBN:
Category :
Languages : en
Pages : 472

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Book Description
Generally, Markov-Gibbs random field (MGRF) models have been success fully used for modelling spatial interactions between various sites of an image. In this dissertation, four novel MGRF models based on calculating the co-occurrences of the observed signals (gray levels) rather than differences will be presented. Moreover, new approaches of accurate identification (estimation of the Gibbs potentials, and the locations of the neighborhood system) for these four models will be introduced. The generic rotation-scaling variant MGRF model is useful for image alignment, whereas the proposed rotation-invariant model is useful for tracking and segmenting moving objects that have small rotational changes from one frame to another. Moreover, this model can be very useful in segmenting objects such as lung nodules, colon polyps, and brain tumors as these objects have a particular appearance model but may appear in different orientation. Furthermore, new analytical parameter estimates for conventional auto-binomial MGRF and joint MGRF of images and region maps are considered for use in solving image segmentation problems.

Author: Aly A. Farag
Publisher: Cambridge University Press
ISBN: 1139991469
Category : Technology & Engineering
Languages : en
Pages : 486

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Book Description
Ideal for classroom use and self-study, this book explains the implementation of the most effective modern methods in image analysis, covering segmentation, registration and visualisation, and focusing on the key theories, algorithms and applications that have emerged from recent progress in computer vision, imaging and computational biomedical science. Structured around five core building blocks - signals, systems, image formation and modality; stochastic models; computational geometry; level set methods; and tools and CAD models - it provides a solid overview of the field. Mathematical and statistical topics are presented in a straightforward manner, enabling the reader to gain a deep understanding of the subject without becoming entangled in mathematical complexities. Theory is connected to practical examples in x-ray, ultrasound, nuclear medicine, MRI and CT imaging, removing the abstract nature of the models and assisting reader understanding.

Author: Jiri Jan
Publisher: CRC Press
ISBN: 135138791X
Category : Medical
Languages : en
Pages : 574

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Book Description
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