Unsupervised Image Segmentation Using a Telegraph Parameterization of Pickard Random Field

Goussard, Yves; Idier, Jérôme; Decesare, A.

doi:10.1007/978-1-4613-0147-9_6

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Following [27], parameter vector can be partitioned into two subvectors and which respectively control the conditional pdfs and . Then, as shown in Appendix I, the M-step (11b) can be divided into two operations: maximization of with respect to and maximization of with respect to (see (19) and (20) for the definitions of and , respectively).…”

Section: B Em-based Strategymentioning

confidence: 99%

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

Ciuciu

Poline

Marrelec

et al. 2003

IEEE Trans. Med. Imaging

115

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This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

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Section: B Em-based Strategymentioning

confidence: 99%

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

Ciuciu

Poline

Marrelec

et al. 2003

IEEE Trans. Med. Imaging

115

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show abstract

“…This model was adapted by Haslett (1985), Idier et al (2001), Fjortoft et al (2003), and others for image processing, where they assumed that given a pixel x all its four adjacent pixels y 1 , y 2 , y 3 and y 4 (i.e., the upper, left, right, and underlying adjacent pixels) in a neighborhood like ⎛ ⎝ y 1 y 2 x y 3 y 4 ⎞ ⎠…”

Section: Conditional Independence Of Sparse Datamentioning

confidence: 99%

A single-chain-based multidimensional Markov chain model for subsurface characterization

Zhang

2007

Environ Ecol Stat

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Multidimensional Markov chain models in geosciences were often built on multiple chains, one in each direction, and assumed these 1-D chains to be independent of each other. Thus, unwanted transitions (i.e., transitions of multiple chains to the same location with unequal states) inevitably occur and have to be excluded in estimating the states at unobserved locations. This consequently may result in unreliable estimates, such as underestimation of small classes (i.e., classes with smaller than average areas) in simulated realizations. This paper presents a single-chain-based multidimensional Markov chain model for estimation (i.e., prediction and conditional stochastic simulation) of spatial distribution of subsurface formations with borehole data. The model assumes that a single Markov chain moves in a lattice space, interacting with its nearest known neighbors through different transition probability rules in different cardinal directions. The conditional probability distribution of the Markov chain at the location to be estimated is formulated in an explicit form by following the Bayes' Theorem and the conditional independence of sparse data in cardinal directions. Since no unwanted transitions are involved, the model can estimate all classes fairly. Transiogram models (i.e., 1-D continuous Markov transition probability diagrams) are used to provide transition probability input with needed lags to generalize the model. Therefore, conditional simulation can be conducted directly and efficiently. The model provides an alternative for heterogeneity characterization of subsurface formations.

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“…The main advantage of choosing convex potential functions with edge preserving properties is to use halfquadratic optimization algorithms [17,18,19,20]. In fact, it is now well known that, the joint optimization of a criterion such as…”

Section: Fusion Of Geometric Information and Radiographic Datamentioning

confidence: 99%

Bayesian approach with hierarchical Markov modeling for data fusion in image reconstruction applications

Mohammad-Djafari

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

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Unsupervised Image Segmentation Using a Telegraph Parameterization of Pickard Random Field

Cited by 9 publications

References 10 publications

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment

A single-chain-based multidimensional Markov chain model for subsurface characterization

Bayesian approach with hierarchical Markov modeling for data fusion in image reconstruction applications

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