Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies
Résumé
We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In this approach we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way that selects factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.
Domaines
Informatique [cs] Vision par ordinateur et reconnaissance de formes [cs.CV] Informatique [cs] Algorithme et structure de données [cs.DS] Informatique [cs] Imagerie médicale Informatique [cs] Traitement des images [eess.IV] Informatique [cs] Traitement du signal et de l'image [eess.SP] Sciences du Vivant [q-bio] Cancer Sciences du Vivant [q-bio] Ingénierie biomédicale Imagerie Physique [physics] Physique [physics] Physique Médicale [physics.med-ph]
Origine : Fichiers produits par l'(les) auteur(s)