Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model
Signal Processing Conference（2014）
摘要
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 Random Field (MRF) has been incorporated into the method to represent the spatio-temporal coherence of the mixture components. The method is successfully applied to 4-D registered PET-CT data of a patient with lung cancer. Results show that the proposed model fits accurately the data and allows the segmentation of different tissues and the identification of tumors in temporal series.
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关键词
Bayes methods,Markov processes,cancer,computerised tomography,image representation,image segmentation,lung,medical image processing,mixture models,positron emission tomography,spatiotemporal phenomena,tumours,4D registered PET-CT data,Bayesian spatiotemporal segmentation,MRF,bimodal data representation,bivariate-Poisson mixture model,combined PET-CT data,generalized four-dimensional Potts-Markov random field,joint 4D PET-CT image segmentation,joint parameter estimation,lung cancer patient,spatio-temporal coherence representation,temporal series,tissue segmentation,tumor identification,unsupervised algorithm,voxel labelling,4-D segmentation,PET-CT,bivariate Poisson distribution,data fusion,multimodality
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