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Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging

Abstract : Segmenting dynamic contrast enhanced-MRI series of small animal, which are intrinsically noisy and low contrasted images with low resolution, is the aim of this paper. To do this, a segmentation method taking into account the temporal (spectral) and spatial information is presented on several series. The idea is to start from a temporal classification, and to build a probability density function of contours conditionally to this classification. Then, this function is segmented to find potentially tumorous areas. The method is presented on several series after a range normalization histogram in order to compare the series.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00834030
Contributeur : Guillaume Noyel Connectez-vous pour contacter le contributeur
Soumis le : mardi 23 mai 2017 - 09:39:00
Dernière modification le : samedi 22 octobre 2022 - 05:18:26
Archivage à long terme le : : vendredi 25 août 2017 - 00:15:40

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Guillaume Noyel, Jesus Angulo, Dominique Jeulin, D. Balvay, C. A. Cuénod. Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), May 2008, Paris, France. pp.1517-1520, ⟨10.1109/ISBI.2008.4541297⟩. ⟨hal-00834030v2⟩

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