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Article dans une revue
Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies
Abstract : 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.
https://hal-mines-paristech.archives-ouvertes.fr/hal-01152401
Contributeur : Guillaume Noyel <>
Soumis le : vendredi 25 octobre 2019 - 15:01:10
Dernière modification le : jeudi 24 septembre 2020 - 16:38:04
Archivage à long terme le : : dimanche 26 janvier 2020 - 16:01:05
Contributeur : Guillaume Noyel <>
Soumis le : vendredi 25 octobre 2019 - 15:01:10
Dernière modification le : jeudi 24 septembre 2020 - 16:38:04
Archivage à long terme le : : dimanche 26 janvier 2020 - 16:01:05
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