We present in this paper a novel approach for the coarse segmentation
of tubular structures in 3D image data. Our
approach, which requires only few initial values and little user
interaction, can be used to initialise complex deformable models and
is based on an extension of
the randomized Hough transform (RHT), a robust method for
low-dimensional parametric object detection. In combination with a
discrete Kalman filter, tubular structures, modelled as generalized
cylinders, are tracked through 3D space. Our extensions to the RHT
feature adaptive selection of the
sample size, expectation-dependent weighting of the input data, and a
novel 3D parameterisation for straight elliptical
cylinders. Experimental results obtained for 3D synthetic as well as
for 3D 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 or the spinal chord.
