A Non-Local Fuzzy Segmentation Method: Application to Brain MRI

Authors:
Benoît Caldairou;François Rousseau;Nicolas Passat;Piotr Habas;Colin Studholme;Christian Heinrich
Affiliations:
LSIIT, UMR 7005 CNRS-Université de Strasbourg, Illkirch, France 67412;LSIIT, UMR 7005 CNRS-Université de Strasbourg, Illkirch, France 67412;LSIIT, UMR 7005 CNRS-Université de Strasbourg, Illkirch, France 67412;Biomedical Image Computing Group, University of California San Francisco, San Francisco, USA 94143;Biomedical Image Computing Group, University of California San Francisco, San Francisco, USA 94143;LSIIT, UMR 7005 CNRS-Université de Strasbourg, Illkirch, France 67412
Venue:
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Year:
2009

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Abstract

The Fuzzy C-Means algorithm is a widely used and flexible approach for brain tissue segmentation from 3D MRI. Despite its recent enrichment by addition of a spatial dependency to its formulation, it remains quite sensitive to noise. In order to improve its reliability in noisy contexts, we propose a way to select the most suitable example regions for regularisation. This approach inspired by the Non-Local Mean strategy used in image restoration is based on the computation of weights modelling the grey-level similarity between the neighbourhoods being compared. Experiments were performed on MRI data and results illustrate the usefulness of the approach in the context of brain tissue classification.