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A comparison of non-parametric segmentation methods

Bruno Sciolla 1, * Paola Ceccato 1 Thibaut Dambry 2 Benoît Guibert 2 Philippe Delachartre 1
* Corresponding author
1 Imagerie Ultrasonore
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : In image segmentation, level-set methods discriminating regions with Parzen estimates of their intensity distributions have proven useful in a broad variety of contexts. A number of area cost terms have been proposed to achieve this goal, such as log-likelihood, Bhattacharyya coefficient, Kullback-Leibler divergence and several others. In this work we compare the performance of the most widespread criterions and show that log-likelihood and assimilated methods have a clear advantage in terms of robustness. In particular, the other methods tested suffer from a boundary instability due to small region/small initialization/hard to distinguish regions. We also give some theoretical arguments supporting our experimental results on synthetic and real images.
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Submitted on : Tuesday, April 26, 2016 - 2:08:14 PM
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  • HAL Id : hal-01307318, version 1


Bruno Sciolla, Paola Ceccato, Thibaut Dambry, Benoît Guibert, Philippe Delachartre. A comparison of non-parametric segmentation methods. Colloque GRETSI 2015, Sep 2015, Lyon, France. pp.4. ⟨hal-01307318⟩



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