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Bayesian Approach to Image Interpretation

Author: Sunil K. Kopparapu

Publisher: Springer Science & Business Media

Published: 2005-11-25

Total Pages: 137

ISBN-13: 0306469960

Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition.

A Bayesian Approach to Parametric Image Analysis

Author: Mary Elizabeth Spilker

Publisher:

Published: 2002

Total Pages: 346

ISBN-13:

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Introduction to Bayesian Image Analysis

Author:

Publisher:

Published: 1993

Total Pages: 16

ISBN-13:

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The basic concepts in the application of Bayesian methods to image analysis are introduced. The Bayesian approach has benefits in image analysis and interpretation because it permits the use of prior knowledge concerning the situation under study. The fundamental ideas are illustrated with a number of examples ranging from a problem in one and two dimensions to large problems in image reconstruction that make use of sophisticated prior information.

Bayesian and grAphical Models for Biomedical Imaging

Author: M. Jorge Cardoso

Publisher: Springer

Published: 2014-09-22

Total Pages: 139

ISBN-13: 3319122894

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This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.

Dermoscopy Image Analysis

Author: M. Emre Celebi

Publisher: CRC Press

Published: 2015-10-16

Total Pages: 458

ISBN-13: 1482253275

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Dermoscopy is a noninvasive skin imaging technique that uses optical magnification and either liquid immersion or cross-polarized lighting to make subsurface structures more easily visible when compared to conventional clinical images. It allows for the identification of dozens of morphological features that are particularly important in identifyin

Information Processing in Medical Imaging

Author: Michael F. Insana

Publisher: Springer

Published: 2003-06-29

Total Pages: 553

ISBN-13: 3540457291

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This book constitutes the refereed proceedings of the 17th International Conference on Information Processing in Medical Imaging, IPMI 2001, held in Davis, CA, USA, in June 2001. The 54 revised papers presented were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on objective assessment of image quality, shape modeling, molecular and diffusion tensor imaging, registration and structural analysis, functional image analysis, fMRI/EEG/MEG, deformable registration, shape analysis, and analysis of brain structure.

Stochastic Models, Statistical Methods, and Algorithms in Image Analysis

Author: Piero Barone

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 266

ISBN-13: 1461229200

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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.

Current Trends in Bayesian Methodology with Applications

Author: Satyanshu K. Upadhyay

Publisher: CRC Press

Published: 2015-05-21

Total Pages: 674

ISBN-13: 1482235129

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Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples. This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.

NEW HIERARCHICAL BAYESIAN APPR

Author: Bo-Kei Woo

Publisher: Open Dissertation Press

Published: 2017-01-27

Total Pages: 112

ISBN-13: 9781374769663

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This dissertation, "A New Hierarchical Bayesian Approach to Low-field Magnetic Resonance Imaging" by Bo-kei, Woo, 胡寶琦, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3122691 Subjects: Bayesian statistical decision theory Magnetic resonance imaging Image analysis - Statistical methods

Image Analysis, Random Fields and Dynamic Monte Carlo Methods

Author: Gerhard Winkler

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 321

ISBN-13: 3642975224

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This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as 'Bayesian Image Analysis'. There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such 'classical' applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW, U. GRENANDER and D.M. KEENAN (1987), (1990) strongly support this belief.