Analysis of magnetic resonance images (MRI) of the brain permits the identification and measurement of brain compartments. These compartments include normal subdivisions of brain tissue, such as gray matter, white matter and specific structures, and also include pathologic lesions associated with stroke or viral infection. A fuzzy system has been developed to analyze images of animal and human brain, segmenting the images into physiologically meaningful regions for display and measurement. This image segmentation system consists of two stages which include a fuzzy rule-based system and fuzzy c-means algorithm (FCM). The first stage of this system is a fuzzy rule-based system which classifies most pixels in MR images into several known classes and one `unclassified' group, which fails to fit the predetermined rules. In the second stage, this system uses the result of the first stage as initial estimates for the properties of the compartments and applies FCM to classify all the previously unclassified pixels. The initial prototypes are estimated by using the averages of the previously classified pixels. The combined processes constitute a fast, accurate and robust image segmentation system. This method can be applied to many clinical image segmentation problems. While the rule-based portion of the system allows specialized knowledge about the images to be incorporated, the FCM allows the resolution of ambiguities that result from noise and artifacts in the image data. The volumes and locations of the compartments can easily be measured and reported quantitatively once they are identified. It is easy to adapt this approach to new imaging problems, by introducing a new set of fuzzy rules and adjusting the number of expected compartments. However, for the purpose of building a practical fully automatic system, a rule learning mechanism may be necessary to improve the efficiency of modification of the fuzzy rules.