Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model. Irace, Z. & Batatia, H. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 2095-2099, Sep., 2014.
Paper abstract bibtex 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.
@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},
}
Downloads: 0
{"_id":"sXMLfxdPRtQ9RSio8","bibbaseid":"irace-batatia-bayesianspatiotemporalsegmentationofcombinedpetctdatausingabivariatepoissonmixturemodel-2014","authorIDs":[],"author_short":["Irace, Z.","Batatia, H."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Z."],"propositions":[],"lastnames":["Irace"],"suffixes":[]},{"firstnames":["H."],"propositions":[],"lastnames":["Batatia"],"suffixes":[]}],"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","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. 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.},\n 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},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926967.pdf},\n}\n\n","author_short":["Irace, Z.","Batatia, H."],"key":"6952759","id":"6952759","bibbaseid":"irace-batatia-bayesianspatiotemporalsegmentationofcombinedpetctdatausingabivariatepoissonmixturemodel-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926967.pdf"},"keyword":["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"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.755Z","downloads":0,"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"],"search_terms":["bayesian","spatiotemporal","segmentation","combined","pet","data","using","bivariate","poisson","mixture","model","irace","batatia"],"title":"Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}