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




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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 : angiogenesis imaging image enhancement filtering theory image classification Dynamic Contrast Enhanced MRI image segmentation mathematical morphology medical image processing segmentation Multivariate images hyperspectral images probability tumours biomedical MRI blood vessels cancer edge detection





Author: Guillaume Noyel - Jesus Angulo - Dominique Jeulin - D. Balvay - C. A. Cuénod -

Source: https://hal.archives-ouvertes.fr/



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