Title :
Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging
Author :
Noyel, G. ; Angulo, J. ; Jeulin, D. ; Balvay, D. ; Cuenod, CA
Author_Institution :
Centre de Morphologie Math., Ecole des Mines de Paris, Paris
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 :
biomedical MRI; blood vessels; cancer; edge detection; filtering theory; image classification; image enhancement; image segmentation; mathematical morphology; medical image processing; probability; tumours; DCE-MRI series; angiogenesis imaging; contour detection; dynamic contrast enhanced-MRI; hyperspectral mathematical morphology; image filtering; image segmentation; probability density function; range normalization histogram; region classification; spatial information; spectral information; temporal classification; temporal information; tumorous areas; Animals; Filtering; Hyperspectral imaging; Image resolution; Image segmentation; Magnetic resonance imaging; Morphology; Pixel; Probability density function; Spatial resolution; Hyperspectral images; MRI; angiogenesis imaging; mathematical morphology; segmentation;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-2002-5
Electronic_ISBN :
978-1-4244-2003-2
DOI :
10.1109/ISBI.2008.4541297
