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Conference papers
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.
Keywords :
Dynamic Contrast Enhanced MRI
image segmentation
Mathematical Morphology
medical image processing
segmentation
Multivariate images
edge detection
cancer
blood vessels
biomedical MRI
tumours
probability
hyperspectral images
angiogenesis imaging
filtering theory
image enhancement
image classification
Machine learning
https://hal-mines-paristech.archives-ouvertes.fr/hal-00834030
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
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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