@inproceedings{e3adeb188b524ff2be17a931b470e148,
title = "Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model",
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 = "4-D segmentation, bivariate Poisson distribution, data fusion, multimodality, PET-CT",
author = "Zacharie Irace and Hadj Batatia",
year = "2014",
month = nov,
day = "13",
language = "English",
series = "European Signal Processing Conference",
publisher = "IEEE",
pages = "2095--2099",
booktitle = "2014 22nd European Signal Processing Conference (EUSIPCO)",
address = "United States",
note = "22nd European Signal Processing Conference 2014, EUSIPCO 2014 ; Conference date: 01-09-2014 Through 05-09-2014",
}
