Using an Extended Hough Transform Combined with a Kalman Filter to Segment Tubular Structures in 3D Medical Images

We present a new approach for the coarse segmentation of tubular structures in 3D image data. Our algorithm, which requires only few initial values and minimal 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. By means of 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.