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Conference Papers Year : 2009

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

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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.
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Dates and versions

hal-01695014 , version 1 (03-03-2018)

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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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