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Tumor classification and prediction using robust multivariate clustering of multiparametric MRI

Alexis Arnaud 1 Florence Forbes 1 Benjamin Lemasson 2 Emmanuel Luc Barbier 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 Equipe 5 : NeuroImagerie Fonctionnelle et Perfusion Cérébrale
UJF - Université Joseph Fourier - Grenoble 1, CEA - Commissariat à l'énergie atomique et aux énergies alternatives, INSERM - Institut National de la Santé et de la Recherche Médicale : U836, [GIN] Grenoble Institut des Neurosciences
Abstract : In neuro-oncology, the use of multiparametric MRI may better characterize brain tumor heterogeneity. To fully exploit multiparametric MRI (e.g. tumor classification), appropriate analysis methods are yet to be developed. In this work, we show on small animals data that advanced statistical learning approaches can help 1) in organizing existing data by detecting and excluding outliers and 2) in building a dictionary of tumor fingerprints from a clustering analysis of their microvascular features.
Keywords : EM algorithm Mixture model Statistics Tumor classification multiparametric MRI Robust clustering multivariate Student distribution
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https://hal.archives-ouvertes.fr/hal-01253584
Contributor : Alexis Arnaud <>
Submitted on : Monday, January 11, 2016 - 9:27:38 AM
Last modification on : Thursday, March 26, 2020 - 8:49:32 PM
Document(s) archivé(s) le : Tuesday, April 12, 2016 - 11:06:08 AM

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  • HAL Id : hal-01253584, version 1

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Alexis Arnaud, Florence Forbes, Benjamin Lemasson, Emmanuel Luc Barbier. Tumor classification and prediction using robust multivariate clustering of multiparametric MRI. International Society for Magnetic Resonance in Medicine, May 2015, Toronto, Canada. ⟨hal-01253584⟩

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