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

Guillaume Noyel 1 Jesus Angulo 1 Dominique Jeulin 1 D. Balvay 2, 3 C. A. Cuénod 2, 3
1 CMM - Centre de Morphologie Mathématique
2 LRI-EA4062
3 Service de radiologie (AP-HP - HEGP)
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.
Keywords : filtering theory image enhancement angiogenesis imaging hyperspectral images probability tumours biomedical MRI blood vessels cancer edge detection Multivariate images segmentation medical image processing Mathematical Morphology image segmentation Dynamic Contrast Enhanced MRI image classification
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00834030
Contributeur : Guillaume Noyel <>
Soumis le : mardi 23 mai 2017 - 09:39:00
Dernière modification le : mercredi 28 août 2019 - 10:37:23
Document(s) archivé(s) 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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