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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.
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Soumis le : vendredi 25 octobre 2019 - 15:01:10
Dernière modification le : jeudi 24 septembre 2020 - 16:38:04
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Guillaume Noyel, Jesus Angulo, Dominique Jeulin, Daniel Balvay, Charles-André Cuenod. Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies. Image Analysis and Stereology, International Society for Stereology, 2015, 34 (1), pp.1-25. ⟨10.5566/ias.1109⟩. ⟨hal-01152401v2⟩



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