@InProceedings{6952759,
  author = {Z. Irace and H. Batatia},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model},
  year = {2014},
  pages = {2095-2099},
  abstract = {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.},
  keywords = {Bayes methods;cancer;computerised tomography;image representation;image segmentation;lung;Markov processes;medical image processing;mixture models;positron emission tomography;spatiotemporal phenomena;tumours;Bayesian spatiotemporal segmentation;combined PET-CT data;unsupervised algorithm;joint 4D PET-CT image segmentation;bivariate-Poisson mixture model;bimodal data representation;voxel labelling;joint parameter estimation;generalized four-dimensional Potts-Markov random field;MRF;spatio-temporal coherence representation;4D registered PET-CT data;lung cancer patient;tissue segmentation;tumor identification;temporal series;Positron emission tomography;Computed tomography;Image segmentation;Data models;Tumors;Bayes methods;Lungs;multimodality;data fusion;4-D segmentation;PET-CT;bivariate Poisson distribution},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926967.pdf},
}

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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.","keywords":"Bayes methods;cancer;computerised tomography;image representation;image segmentation;lung;Markov processes;medical image processing;mixture models;positron emission tomography;spatiotemporal phenomena;tumours;Bayesian spatiotemporal segmentation;combined PET-CT data;unsupervised algorithm;joint 4D PET-CT image segmentation;bivariate-Poisson mixture model;bimodal data representation;voxel labelling;joint parameter estimation;generalized four-dimensional Potts-Markov random field;MRF;spatio-temporal coherence representation;4D registered PET-CT data;lung cancer patient;tissue segmentation;tumor identification;temporal series;Positron emission tomography;Computed tomography;Image segmentation;Data models;Tumors;Bayes methods;Lungs;multimodality;data fusion;4-D segmentation;PET-CT;bivariate Poisson distribution","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926967.pdf","bibtex":"@InProceedings{6952759,\n author = {Z. Irace and H. Batatia},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model},\n year = {2014},\n pages = {2095-2099},\n abstract = {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. 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