Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging
Résumé
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.
Mots clés
- edge detection
- probability
- Multivariate images
- segmentation
- medical image processing
- Mathematical Morphology
- image segmentation
- Dynamic Contrast Enhanced MRI
- image classification
- Machine learning
- cancer
- blood vessels
- biomedical MRI
- tumours
- hyperspectral images
- angiogenesis imaging
- image enhancement
- filtering theory
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