Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model

Zacharie Irace, Hadj Batatia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2014 22nd European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages2095-2099
Number of pages5
ISBN (Electronic)9780992862619
Publication statusPublished - 13 Nov 2014
Event22nd European Signal Processing Conference 2014 - Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

Conference22nd European Signal Processing Conference 2014
Abbreviated titleEUSIPCO 2014
CountryPortugal
CityLisbon
Period1/09/145/09/14

Keywords

  • 4-D segmentation
  • bivariate Poisson distribution
  • data fusion
  • multimodality
  • PET-CT

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Bayesian spatiotemporal segmentation of combined PET-CT data using a bivariate poisson mixture model'. Together they form a unique fingerprint.

Cite this