Bayesian Spatiotemporal Segmentation of Combined PET-CT Data Using a Bivariate Poisson Mixture Model

Zacharie Irace (1) , Hadj Batatia (1)

1 IRIT-TCI - Traitement et Compréhension d’Images

The 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.

Mots clés: multimodality; data fusion; 4-D segmentation; PET-CT; bivariate Poisson distribution

Domaines: Informatique [cs]

Zacharie Irace, Hadj Batatia. Bayesian Spatiotemporal Segmentation of Combined PET-CT Data Using a Bivariate Poisson Mixture Model. 22rd European Signal and Image Processing Conference (EUSIPCO 2014), European Association for Signal Processing (EURASIP), Sep 2014, Lisbonne, Portugal. pp.1--6. ⟨hal-03256422⟩