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Enhanced representation & learning of magnetic resonance signatures in multiple sclerosis

Yogesh Karpate 1 
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Multiple Sclerosis (MS) is an acquired inflammatory disease, which causes disabilities in young adults and it is common in northern hemisphere. This PhD work focuses on characterization and modeling of multidimensional MRI signatures in MS Lesions (MSL). The objective is to improve image representation and learning for visual recognition, where high level information such as MSL contained in MRI are automatically extracted. We propose a new longitudinal intensity normalization algorithm for multichannel MRI in the presence of MS lesions, which provides consistent and reliable longitudinal detections. This is primarily based on learning the tissue intensities from multichannel MRI using robust Gaussian Mixture Modeling. Further, we proposed two MSL detection methods based on a statistical patient to population comparison framework and probabilistic one class learning. We evaluated our proposed algorithms on multi-center databases to verify its efficacy.
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Submitted on : Monday, March 7, 2016 - 4:12:16 PM
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  • HAL Id : tel-01270533, version 2


Yogesh Karpate. Enhanced representation & learning of magnetic resonance signatures in multiple sclerosis. Medical Imaging. Université Rennes 1, 2015. English. ⟨NNT : 2015REN1S068⟩. ⟨tel-01270533v2⟩



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