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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Caldairou, B., Rousseau, F., Passat, N., Habas, P., Studholme, C., & Heinrich, C. (2009). A non-local fuzzy segmentation method: Application to brain MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5702 LNCS, pp. 606–613). https://doi.org/10.1007/978-3-642-03767-2_74
Mendeley helps you to discover research relevant for your work.