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We present a novel approach to the coarse segmentation of tubular structures in three-dimensional (3-D) image data. Our algorithm, which requires only few initial values and minimal user interaction, can be used to initialize complex deformable models and is based on an extension of the randomized hough transform (RHT), a robust method for low-dimensional parametric object detection. Tubular structures are modeled as generalized cylinders. By means of a discrete Kalman filter, they are tracked through 3-D space. Our extensions to the RHT are a feature adaptive selection of the sample size, expectation-dependent weighting of the input data, and a novel 3-D parameterization for straight elliptical cylinders. Experimental results obtained for 3-D synthetic as well as for 3-D medical images demonstrate the robustness of our approach w.r.t. image noise. We present the successful segmentation of tubular anatomical structures such as the aortic arc and the spinal cord.