Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Benboudjema, Dalila; Pieczynski, Wojciech

doi:10.1109/tpami.2007.1059

Cited by 78 publications

(54 citation statements)

References 33 publications

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Section: Discussionmentioning

confidence: 99%

“…The algorithm should take place in the context of reversible jumps MCMC to properly handle fusion/segragation moves between parcels Green (1995); Richardson and Green (1997). Besides, the parcellation identification issue could also be attacked using triplet Markov fields (Benboudjema and Pieczynski, 2007), which seem suitable for modelling nonstationarities in image segmentation.A strong feature of our approach is the possibility to derive parcel-based HRF time courses throughout the brain. It allows us to assess the spatial variability of the HRF shape and to check that this shape greatly fluctuates across parcels.…”

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confidence: 99%

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A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Idier²,

et al. 2008

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Within-subject analysis in fMRI essentially addresses two problems, i.e., the detection of activated brain regions in response to an experimental task and the estimation of the underlying dynamics, also known as the characterisation of Hemodynamic response function (HRF). So far, both issues have been treated sequentially while it is known that the HRF model has a dramatic impact on the localisation of activations and that the HRF shape may vary from one region to another. In this paper, we conciliate both issues in a region-based joint detection-estimation framework that we develop in the Bayesian formalism. Instead of considering function basis to account for spatial variability, spatially adaptive General Linear Models are built upon region-based non-parametric estimation of brain dynamics. Regions are first identified as functionally homogeneous parcels in the mask of the grey matter using a specific procedure [Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.-B., August 2006. Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27 (8), 678-693.]. Then, in each parcel, prior information is embedded to constrain this estimation. Detection is achieved by modelling activating, deactivating and nonactivating voxels through mixture models within each parcel. From the posterior distribution, we infer upon the model parameters using Markov Chain Monte Carlo (MCMC) techniques. Bayesian model comparison allows us to emphasize on artificial datasets first that inhomogeneous gamma-Gaussian mixture models outperform Gaussian mixtures in terms of sensitivity/specificity trade-off and second that it is worthwhile modelling serial correlation through an AR(1) noise process at low signal-to-noise (SNR) ratio. Our approach is then validated on an fMRI experiment that studies habituation to auditory sentence repetition. This phenomenon is clearly recovered as well as the hierarchical temporal organisation of the superior temporal sulcus, which is directly derived from the parcel-based HRF estimates. © 2008 Elsevier Inc. All rights reserved.Keywords: Bayesian modelling; fMRI; Gamma-Gaussian mixture model; Detection-estimation; Markov Chain Monte Carlo methods; Bayes factor; Model comparison IntroductionSince the first report of the BOLD effect in human (Ogawa et al., 1990), functional magnetic resonance imaging (fMRI) has represented a powerful tool to non-invasively study the relation between cognitive stimulus and the hemodynamic (BOLD) response. Within-subject analysis in fMRI is usually addressed using a hypothesis-driven approach that actually postulates a model for the HRF and enable voxelwise inference in the General Linear Model (GLM) framework. In this formulation, the modelling of the BOLD response i.e., the definition of the design matrix is crucial. In its simplest form, this matrix relies on a spatially invariant temporal model of the BOLD signal across the brain meaning that the expected response to each stimulus is modelled by ...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Idier²,

et al. 2008

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“…As in the case of EM, convergence can be obtained under some reasonable hypothesis if the initial value θ 0 is close enough to the real value θ. ICE was successfully used in numerous problems [5,28,46,53,59,68], and the contribution [48] is also based on ICE. Let us notice that EM and ICE can give, in particular parametrizations of exponential models, the same sequence [26].…”

Section: Ice Methodsmentioning

confidence: 99%

“…TMC can be extended to Triplet partially Markov chains (TPMC), in which the long memory noise [30] can be taken into account [46]. Let us also mention the triplet Markov fields (TMF) recently applied in textured images segmentation [5,10].…”

Section: Pairwise and Triplet Markov Modelsmentioning

confidence: 99%

New trends in Markov models and related learning to restore data

Forbes¹,

Pieczynski²

2009

2009 IEEE International Workshop on Machine Learning for Signal Processing

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We present recent approaches that extend standard Markov models and increase their modelling power. These capabilities are illustrated in the cited published works and more recently in the contributions to the Special Session on Markov models of the IEEE International Workshop on Machine Learning for Signal Processing, 2009. However, the review is not exhaustive and major older works may be missing.

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“…TMCs have also been used for continuous hidden sequences in Kalman filtering [30], in prediction [31], or still optimal fast filtering in a particular class of switching systems [32]. Finally, let us mention that hidden Markov fields have also been extended to triplet Markov fields [33], and have been successfully applied to complex structure data classification [34], in SAR images processing [35], [36], [37], [38] or biometry [39].…”

Section: Introductionmentioning

confidence: 99%

Phasic Triplet Markov Chains

Boudaren

Monfrini

Pieczynski

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.

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Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Cited by 78 publications

References 33 publications

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

New trends in Markov models and related learning to restore data

Phasic Triplet Markov Chains

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