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We propose a novel variational approach to automatically soft-segment medical images into a fixed number of classes. Our method combines fuzzy classification and active contours in a single variational framework. This approach allows the use of tools from both de-formable geometry and clustering in a well-defined setting and provides a useful, unsupervised segmentation technique. The model was tested on synthetic and MRI brain data, with promising results.