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Spatial models for fuzzy clustering. (English) Zbl 1033.68612
Summary: A novel approach to fuzzy clustering for image segmentation is described. The fuzzy \(C\)-means objective function is generalized to include a spatial penalty an the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy \(C\)-means algorithm and allows the estimation of spatially smooth membership functions. To determine the strength of the penalty function, a criterion based an cross-validation is employed. The new algorithm is applied to simulated and real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

68T10 Pattern recognition, speech recognition
68U99 Computing methodologies and applications
68U10 Computing methodologies for image processing
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