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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https://hal-mines-paristech.archives-ouvertes.fr/hal-01152401
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Dernière modification le : mardi 12 septembre 2017 - 11:41:31
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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, pp.1-25. <http://www.ias-iss.org/ojs/IAS/article/view/1109/956>. <10.5566/ias.1109>. <hal-01152401>

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