Detection of Multiple Sclerosis Lesions using Sparse Representations and Dictionary Learning

Hrishikesh Deshpande 1 Pierre Maurel 1 Christian Barillot 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 : The manual delineation of Multiple Sclerosis (MS) lesions is a challenging task pertaining to the requirement of neurological experts and high intra-and inter-observer variability. It is also time consum-ing because large number of Magnetic Resonance (MR) image slices are needed to obtain 3-D information. Over the last years, various mod-els combined with supervised and unsupervised classification methods have been proposed for segmentation of MS lesions using MR images. Recently, signal modeling using sparse representations (SR) has gained tremendous attention and is an area of active research. SR allows cod-ing data as sparse linear combinations of the elements of over-complete dictionary and has led to interesting image recognition results. The dic-tionary used for sparse coding plays a key role in the classification pro-cess. In this work, we have proposed to learn class specific dictionaries and developed a new classification scheme, to automatically detect MS lesions in 3-D multi-channel MR images.
Type de document :
Communication dans un congrès
2nd International Workshop on Sparsity Techniques in Medical Imaging (STMI), MICCAI 2014, Sep 2014, Boston, MA, United States
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https://hal.inria.fr/hal-01087339
Contributeur : Hrishikesh Deshpande <>
Soumis le : mardi 25 novembre 2014 - 19:21:20
Dernière modification le : jeudi 15 novembre 2018 - 11:57:31
Document(s) archivé(s) le : vendredi 14 avril 2017 - 20:43:44

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  • HAL Id : hal-01087339, version 1

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Hrishikesh Deshpande, Pierre Maurel, Christian Barillot. Detection of Multiple Sclerosis Lesions using Sparse Representations and Dictionary Learning. 2nd International Workshop on Sparsity Techniques in Medical Imaging (STMI), MICCAI 2014, Sep 2014, Boston, MA, United States. 〈hal-01087339〉

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