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This paper presents a Bayesian algorithm for PET image segmentation. The proposed method, which is derived from PET physics, models tissue activity using a mixture of Poisson-Gamma distributions. Moreover, a Markov field is proposed to model the spatial correlation between mixture components. Then, segmentation is performed using an Markov chain Monte Carlo(More)
This paper deals with the restoration of Positron Emission Tomography images. The partial volume effect creates blurring in such images and causes inaccurate quantization. This artefact is due to the complex geometry of the acquisition system. We propose to represent this complexity by a spatially variable point spread function. The PSF is first measured at(More)
This paper presents an unsupervised algorithm for the joint segmentation of 4-D PET-CT images. The proposed method is based on a bivariate-Poisson mixture model to represent the bimodal data. A Bayesian framework is developed to label the voxels as well as jointly estimate the parameters of the mixture model. A generalized four-dimensional Potts-Markov(More)
Cet article présente un algorithme Bayésien pour la seg-mentation d'images de Tomographie par Emission de Po-sitons (TEP). Tenant compte des phénomènes physiques sous-jacents à la formation de l'image TEP, nous mo-délisons l'activité des tissus comme un mélange de distributions Poisson-Gamma. Un algorithme Bayésien hié-rarchique de type Monte Carlo par(More)
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