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Your search keyword '"Heavy-tailed Rayleigh distribution"' showing total 2 results
Start Over Descriptor "Heavy-tailed Rayleigh distribution" Topic gibbs sampler
2 results on '"Heavy-tailed Rayleigh distribution"'

1. Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model.

Author

Pereyra, Marcelo, Dobigeon, Nicolas, Batatia, Hadj, and Tourneret, Jean-Yves

Subjects
*TISSUES, *SKIN disease diagnosis, *IMAGE segmentation, *ULTRASONIC imaging, *DISTRIBUTION (Probability theory), *MARKOV chain Monte Carlo, *BAYESIAN analysis
Abstract

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images. [ABSTRACT FROM PUBLISHER]

Published
2012
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2. Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model

Author

Hadj Batatia, Nicolas Dobigeon, Jean-Yves Tourneret, Marcelo Pereyra, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), and Université Toulouse 1 Capitole - UT1 (FRANCE)

Subjects
Skin Neoplasms, Quantitative Biology::Tissues and Organs, Posterior probability, Monte Carlo method, Physics::Medical Physics, Bayesian inference, Models, Biological, symbols.namesake, Imaging, Three-Dimensional, Gibbs sampler, Heavy-tailed Rayleigh distribution, Humans, Traitement du signal et de l'image, Computer Simulation, Electrical and Electronic Engineering, Melanoma, Mathematics, Skin, Ultrasonography, Mixture model, Bayes estimator, Models, Statistical, Radiological and Ultrasound Technology, business.industry, Phantoms, Imaging, Reproducibility of Results, Markov chain Monte Carlo, Pattern recognition, Bayes Theorem, Terms-Bayesian estimation, Image segmentation, Computer Science Applications, Forearm, symbols, Artificial intelligence, Motts–Markov field, business, Software, Algorithms, Gibbs sampling
Abstract

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Published
2012

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