A non-local fuzzy segmentation method: Application to brain MRI

Benoît Caldairou 1 François Rousseau 1 Nicolas Passat 1 Piotr Habas 2 Colin Studholme 2 Christian Heinrich 1
1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
2 Biomedical Image Computing Group
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.
Keywords : fuzzy clustering regularisation non-local processing image segmentation MRI
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Conference papers
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Benoît Caldairou, François Rousseau, Nicolas Passat, Piotr Habas, Colin Studholme, et al.. A non-local fuzzy segmentation method: Application to brain MRI. International Conference on Computer Analysis of Images and Patterns (CAIP), 2009, Münster, Germany. pp.606-613, ⟨10.1007/978-3-642-03767-2_74⟩. ⟨hal-01695014⟩

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