Result: Bayesian models for medical image biology using Monte Carlo Markov chains techniques

Title:
Bayesian models for medical image biology using Monte Carlo Markov chains techniques
Authors:
ZIMERAS, S, GEROGIAKODIS, F
Source:
International conference of computational methods in sciences and engineering 2003 (ICCMSE 2003)Mathematical and computer modelling. 42(7-8):759-768
Publisher Information:
Oxford: Elsevier Science, 2005.
Publication Year:
2005
Physical Description:
print, 19 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Applications, Sciences médicales, Medical sciences, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Algorithme MCMC, MCMC algorithm, Algoritmo MCMC, Analyse assistée, Computer aided analysis, Análisis asistido, Analyse forme, Pattern analysis, Análisis forma, Analyse numérique, Numerical analysis, Análisis numérico, Biologie, Biology, Biología, Champ aléatoire, Random field, Campo aleatorio, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation statistique, Statistical estimation, Estimación estadística, Imagerie médicale, Medical imagery, Imaginería médica, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Méthode stochastique, Stochastic method, Método estocástico, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Sélection modèle, Model selection, Selección modelo, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador, Analyse bayésienne, Vraisemblance
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
University of the Aegean, Department of Statistics and Actuarial-Financial Mathematics, 832 00, Karlovassi, Samos, Greece
University of Piraeus, Department of Statistics and Insurance Science, 80, Karaoli and Dimitriou St., 185 34 Piraeus, Greece
ISSN:
0895-7177
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17290797
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.17290797
Database:
PASCAL Archive

Further Information

The objective of Bayesian modelling in pattern analysis is aimed to extract the important characteristics of the pattern using a few parameters so as to represent the pattern effectively. The use of Bayesian methods in medical biology and modelling is an approach, which seeks to provide a unified framework within many different image processes. Markov random fields (M.r.f.) modelling are a very popular pattern analysis methods and it plays an important role in pattern recognition and computer vision. In this work, Bayesian models would be presented to illustrate biological phenomena using the Gibbs sampler technique. Finally, methods for estimating model parameters using likelihood techniques are examined, and a model selection procedure is proposed for classifying the neighbourhood structure of the image. The techniques are investigated using simulated and real data from the area of biology.