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

Guillaume Noyel (1) , Jesus Angulo (1) , Dominique Jeulin (1) , D. Balvay (2, 3) , C. A. Cuénod (2, 3)

1 CMM - Centre de Morphologie Mathématique
2 LRI-EA4062
3 Service de radiologie (AP-HP - HEGP)

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.

Mots clés: 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

Domaines: Informatique [cs]; Imagerie médicale; Sciences du Vivant [q-bio]; Ingénierie biomédicale; Imagerie; Informatique [cs]; Traitement des images [eess.IV]; Sciences du Vivant [q-bio]; Cancer

Guillaume Noyel, Jesus Angulo, Dominique Jeulin, D. Balvay, C. A. Cuénod. Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), May 2008, Paris, France. pp.1517-1520, ⟨10.1109/ISBI.2008.4541297⟩. ⟨hal-00834030v2⟩