We present a segmentation method that combines the robust, accurate, and efficient techniques of fuzzy connectedness with standardized MRI intensities and fast algorithms. The result is a general segmentation framework that more efficiently utilizes the user input (for recognition) and the power of computer (for delineation). This same method has been applied to segment brain tissues from a variety of MRI protocols. Images were corrected for inhomogeneity and standardized to yield tissue-specific intensity values. All parameters for the fuzzy affinity relations were fixed for a specific input protocol. Scale-based fuzzy affinity was used to better capture fine structures. Brain tissues were segmented as 3D fuzzy-connected objects by using relative fuzzy connectedness. The user can specify seed points in about a minute and tracking the 3D fuzzy-connected objects takes about 20 seconds per object. All other computations were performed before any user interaction took place. Segmentation of brain tissues as 3D fuzzy-connected objects from MRI data is feasible at interactive speeds. Utilizing the robust fuzzy connectedness principles and fast algorithms, it is possible to interactively select fuzzy affinity, seed point, and threshold parameters and perform efficient, precise, and accurate segmentations.