This article incorporates fuzzy set theory into the task of image segmentation. The basic concept is to allow the fuzzy membership function to model the uncertainty and vagueness of definition of objects in digital images. We define a fuzzy segmentation as a fuzzy c-partition of an image and incorporate this definition and fuzzy criteria into several image segmentation techniques including segmentation by clustering, region growing, and relaxation labelling. The algorithms are tested on digital forward looking infrared (FLIR) images and digital subtraction angiographic images. These techniques are shown to perform at least as well as their crisp or probabilistic counterparts when converted to a crisp partition. However, the real advantage to a fuzzy methodology is that the degree of membership provides a model of uncertainty and can subsequently be used by feature extraction and object recognition algorithms to increase the amount of information available in decision processes.

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