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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 :
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
Machine learning
https://hal-mines-paristech.archives-ouvertes.fr/hal-00834030
Contributor : Guillaume Noyel Connect in order to contact the contributor
Submitted on : Tuesday, May 23, 2017 - 9:39:00 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:14 PM
Long-term archiving on: : Friday, August 25, 2017 - 12:15:40 AM
Contributor : Guillaume Noyel Connect in order to contact the contributor
Submitted on : Tuesday, May 23, 2017 - 9:39:00 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:14 PM
Long-term archiving on: : Friday, August 25, 2017 - 12:15:40 AM
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