Unsupervised multiband image segmentation using hidden Markov quadtree and copulas

Flitti, Farid; Collet, Christophe; Joannic-Chardin, A.

doi:10.1109/icip.2005.1530135

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…In this context, we can thus envisage the adaptation of other kinds of copulas, whose general theory can be seen in [29], to the Markov field models considered in this paper. In fact, the recent use of copulas in Markov chains [4] or multiband Markov trees [12] has provided encouraging results. Otherwise, multispectral or hyperspectral imagery, as in [28], among others, also offers further possibilities for development of the models and related processing proposed in this paper.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Benboudjema

Pieczynski

2007

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.

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Section: Discussionmentioning

confidence: 99%

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Benboudjema

Pieczynski

2007

IEEE Trans. Pattern Anal. Mach. Intell.

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“…We considered HMC but other hidden Markov models, like trees or fields, could be used in a similar way: the first results obtained with hidden Markov trees seem very promising [17].…”

Section: Discussionmentioning

confidence: 99%

Modeling and Unsupervised Classification of Multivariate Hidden Markov Chains With Copulas

Brunel

Lapuyade‐Lahorgue²,

Pieczynski³

2010

IEEE Trans. Automat. Contr.

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Abstract-Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as Spherically Invariant Random Vectors (SIRVs). In this work, we introduce an alternative solution based on copulas that enables theoretically to represent any multivariate distribution. Estimation procedures are proposed for some mixtures of copula-based densities and are compared in the hidden Markov chain setting, in order to perform statistical unsupervised classification of signals or images. Useful copulas and SIRV for multivariate signal classification are particularly studied through experiments Index Terms-Copulas, EM algorithm, hidden Markov chains, hidden Markov models, inference for margins, maximum likelihood, multivariate modeling, spherically invariant random vector (SIRV), statistical classification.

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“…The lack of incorporating spatial characteristics can be solved through using MRF model (Held, et al, 1997;Flitti, et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

Tavares

Jorge

et al. 2010

Computer Methods in Biomechanics and Biomedical Engineering

270

142

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This paper aims to make a review on the current segmentation algorithms used for medical images.Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused due to the intensive investigations on the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarized. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.

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Unsupervised multiband image segmentation using hidden Markov quadtree and copulas

Cited by 16 publications

References 6 publications

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Modeling and Unsupervised Classification of Multivariate Hidden Markov Chains With Copulas

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

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