Alpha-divergence maximization for statistical region based active contour segmentation with non-parametric PDF estimations

Leila Meziou 1 Aymeric Histace 1, * Frédéric Precioso 2
* Auteur correspondant
1 ICI
ETIS - Equipes Traitement de l'Information et Systèmes
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this article, a complete original framework for non supervised statistical region based active contour segmentation is proposed. More precisely, the method is based on the maximization of alphadivergences between non paramterically estimated probability density functions (PDF) of the inner and outer regions defined by the evolving curve. In this paper, we define the variational context associated to distance maximization in the particular case of alphadivergence and we also provide the complete derivation of the partial differential equation leading the segmentation. Results on synthetic data (corrupted with a high level of Gaussian and Poisonian noises) but also on clinical images (X-ray images) show that the proposed non supervised approach improves classical approach of that kind.
Type de document :
Communication dans un congrès
IEEE. International Conference on Acoustic Speech and Signal Processing, Mar 2012, Kyoto, Japan. pp.861-864, 2012
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https://hal.archives-ouvertes.fr/hal-00672258
Contributeur : Aymeric Histace <>
Soumis le : lundi 20 février 2012 - 20:44:34
Dernière modification le : lundi 13 octobre 2014 - 15:43:25

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  • HAL Id : hal-00672258, version 1

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Leila Meziou, Aymeric Histace, Frédéric Precioso. Alpha-divergence maximization for statistical region based active contour segmentation with non-parametric PDF estimations. IEEE. International Conference on Acoustic Speech and Signal Processing, Mar 2012, Kyoto, Japan. pp.861-864, 2012. <hal-00672258>

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