Segmentation of skin lesions in 2D and 3D ultrasound images using a spatiallay coherent generalized Rayleigh mixture model

Marcelo A Pereyra, N Dobigeon, H Batatia, J-Y Tourneret

Research output: Contribution to journalArticle (Academic Journal)peer-review

46 Citations (Scopus)

Abstract

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Original languageEnglish
Pages (from-to)1509-1520
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume31
Issue number8
DOIs
Publication statusPublished - Aug 2012

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