Combining Robust Expectation Maximization and Mean Shift algorithms for Multiple Sclerosis Brain Segmentation

Daniel García-Lorenzo 1, * Sylvain Prima 1 D. Louis Collins 2 Douglas Arnold 2 Sean Patrick Morrissey 1 Christian Barillot 1
* Corresponding author
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
2 Montreal Neurological Institute and Hospital
Abstract : A new algorithm for segmentation of white matter lesions and normal appearing brain tissues in Multiple Sclerosis (MS) is presented. Two different segmentation methods are combined in order to have a better and more meaningful segmentation. On the one hand, a local segmentation approach, the Mean Shift, is used to generate local regions in our images. On the other hand, a variant of the Expectation Maximization is employed to classify these regions as Normal Appearing Brain Tissues (NABT) or lesions. Validation of this method is performed with synthetic and real data. The output is a more powerful algorithm that employs at the same time global and local information to improve image segmentation.
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Daniel García-Lorenzo, Sylvain Prima, D. Louis Collins, Douglas Arnold, Sean Patrick Morrissey, et al.. Combining Robust Expectation Maximization and Mean Shift algorithms for Multiple Sclerosis Brain Segmentation. MICCAI workshop on Medical Image Analysis on Multiple Sclerosis (validation and methodological issues) (MIAMS'2008), Sep 2008, New York, United States. pp.82-91. ⟨inserm-00421706⟩

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