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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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Contributor : Guillaume Noyel <>
Submitted on : Tuesday, May 23, 2017 - 9:39:00 AM
Last modification on : Saturday, December 12, 2020 - 3:12:31 PM
Long-term archiving on: : Friday, August 25, 2017 - 12:15:40 AM

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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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