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FILTERING, SEGMENTATION AND REGION CLASSIFICATION BY HYPERSPECTRAL MATHEMATICAL MORPHOLOGY OF DCE-MRI SERIES FOR ANGIOGENESIS IMAGING
 

Summary: FILTERING, SEGMENTATION AND REGION CLASSIFICATION BY HYPERSPECTRAL
MATHEMATICAL MORPHOLOGY OF DCE-MRI SERIES FOR ANGIOGENESIS IMAGING
G. Noyel, J. Angulo, D. Jeulin
Centre de Morphologie Math´ematique
Ecole des Mines de Paris
35 rue Saint Honor´e, 77305 Fontainebleau, France
D. Balvay, C-A. Cuenod
LRI-EA4062 Paris V Descartes
APHP, HEGP, Service de Radiologie
Paris, France
ABSTRACT
Segmenting dynamic contrast enhanced-MRI series of small
animal, which are intrinsically noisy and low contrasted im-
ages with low resolution, is the aim of this paper. To do
this, a segmentation method taking into account the tempo-
ral (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 condition-
ally to this classification. Then, this function is segmented to
find potentially tumorous areas. The method is presented on

  

Source: Angulo,Jesús - Centre de Morphologie Mathématique, Ecole des Mines de Paris

 

Collections: Computer Technologies and Information Sciences